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In this paper, we study a dual-hop wireless powered communication network (DH-WPCN), where the two-way communication between the users and the access point (AP) is assisted by a relay. Assuming a piece-wise linear energy harvesting (EH) model for the users and the relay, we investigate the problem of total throughput maximization for both amplify-and-forward (AF) and decode-and-forward (DF) relaying modes. To deal with the non-convexity of the throughput maximization problem, we apply the block coordinate descent (BCD) technique and alternately optimize different blocks of optimization variables until convergence is achieved. Numerical results demonstrate the substantial performance gain of the proposed scheme with piece-wise linear EH model as compared to the case with oversimplified linear EH model. This is due to the fact that our proposed scheme takes into consideration the saturation effect of practical EH circuits in optimizing the resource allocation.

This paper studies the integration of the newly-emerged intelligent reflecting surface (IRS) technology into non-orthogonal multiple access (NOMA)-based wireless powered communication networks (WPCNs). We consider two WPCNs which communicate with a common hybrid access point (HAP), where there exists two types of devices in each WPCN, namely information receiving device (IRD) and harvest-then-transmit device (HTTD). Downlink communication from the HAP to IRDs, downlink energy transfer (ET) from the HAP to HTTDs, and uplink information transmission (IT) from the HTTDs to the HAP are assisted by two IRSs, one in each WPCN. Under this setup, we propose efficient algorithms to optimize reflection coefficients, beamforming vectors, and resource allocation for the sake of uplink sum-rate maximization, taking into account the minimum rate requirement at the IRDs. Numerical results show the considerable performance gain of the proposed NOMA-based scheme as compared to the conventional orthogonal multiple access (OMA)-based counterpart.

Integration of backscatter communication and wireless powered communication is a promising way to improve the performance of future Internet of Things (IoT) networks. However, this integration needs to be studied in practical scenarios with realistic models for the network elements. In this paper, we investigate optimal resource allocation in a backscatter assisted wireless powered communication network (BS-WPCN), where a piece-wise linear energy harvesting (EH) model is assumed at the users, which accounts for the saturation effect of practical energy harvesters. We study the problem of throughput maximization and apply a block coordinate descent (BCD) method to find the optimal allocation of resources for maximizing the total throughput. Numerical results demonstrate that the proposed scheme considerably improves the performance compared to the baseline scheme, which assumes linear EH model at the users.

Wireless powered communication (WPC), where the required energy for communication is obtained via radio frequency (RF) energy harvesting, is a promising technology for the upcoming self-sustainable Internet of Things (IoT) networks. In this paper, a multiuser IoT network is considered, where an energy-constrained relay assists the information transmission of a number of IoT devices to the access point (AP) using WPC. In particular, the relay acquires the energy needed for information forwarding from the RF energy transfer of the AP, which serves as a dedicated energy transmitter for the relay. The objective is to maximize the total network throughput by jointly optimizing the wireless energy transfer (WET) duration and the relay’s energy expenditure in each time slot, subject to the energy causality constraint, for both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. We show that the optimization problems for both the AF- and DF-based systems are convex and thus can be solved using convex optimization techniques. Our analysis shows that the solution of the sum-throughput maximization problems depends on the scheduling order of the IoT devices and the optimal solution is derived for different ordering scenarios. Finally, numerical simulations are presented to corroborate our analysis.

Intelligent reflecting surface (IRS) has recently been emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values., This work has been accepted for publication in IEEE TVT

This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS., To appear in IEEE Transactions on Communications

With the ever-increasing speed of technological advances, more and more objects are being connected to the Internet every day and the world is moving toward the next generation of the Internet, the so called Internet of Things (IoT). However, numerous challenges need to be addressed for the widespread adoption of the IoT paradigm. Energy scarcity is one of these challenges. Recently, the advent of energy harvesting has relaxed energy limitation concerns. Specifically, RF energy harvesting has attracted a large amount of research due to its clear advantages over other energy harvesting methods. Lately, the extensive research in the RF energy harvesting domain has led to the emergence of wireless powered communication networks (WPCNs), where energy-constrained users are powered by wireless energy transfer of an integrated energy and information access point referred to as a hybrid access point (HAP). In this article, we review the research conducted in the field of WPCN and discuss opportunities for further extensions of these networks. Looking from the IoT point of view, we also present some of the points that must be taken into account for impeccable operation of WPCN in the IoT environment.

In this paper, we study a dual-hop wireless powered communication network, where the communication between a hybrid access point (HAP) and a number of users is assisted by energy-constrained relays, which need to harvest energy from radio-frequency (RF) signals broadcast by the HAP. In order to power the relays for assisting the uplink communication, the HAP broadcasts a dedicated energy signal in the downlink. Each relay harvests energy from this signal and utilizes the harvested energy in forwarding its corresponding user's data to the HAP. In downlink communication, relays exploit a power-splitting strategy and use the information signal broadcast by the HAP for both energy harvesting and information forwarding. Under this setup, we are interested in maximizing the total uplink and downlink throughput. First, we propose an algorithm for solving uplink throughput maximization problem, which optimizes time allocations for energy and information transfer. For downlink throughput maximization, we obtain a near-optimal solution by optimizing time allocations and power splitting ratios iteratively. Finally, we conduct numerical simulations to evaluate the performance of the proposed algorithms.

This paper considers a dual-hop wireless powered communication network (DH-WPCN), where the communication between a hybrid access point (HAP) and a number of users is assisted by energy-limited relays. In order to power relays for assisting the communication, the HAP broadcasts a dedicated energy signal in the downlink. Each relay harvests energy from this signal and utilizes the harvested energy in forwarding its corresponding user's data to the HAP with an amplify-and-forward (AF) relaying approach. Under this setup, we study the problem of maximizing the minimum throughput among network users. The objective is to optimize time allocations for energy and information transfer so as to ensure throughput fairness and at the same time optimize the sum-throughput. Simulation results confirm the effectiveness of our proposed fairness enhancement algorithm and reveal the trade-off between sum-throughput and fairness.