In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

In this study, we explore a hybrid non-orthogonal multiple access and time division multiple access (NOMA-TDMA) approach designed to maximize sum throughput in a wireless powered Internet of Things (IoT) network (WPIN). Hybrid access points send energy signals to users on downlink, and users in various groups utilize that harvested energy to transmit information on uplink. This process is facilitated by the NOMA-TDMA scheme wherein users of the same group use NOMA for simultaneous transmissions, and separate time slots are assigned to each group through TDMA. Under this hybrid NOMA-TDMA scheme, the main objective is to enhance the network’s sum throughput by jointly optimizing both the allocation of time for downlink and uplink and the downlink beamforming vectors. Given the complex interdependence of variables, the problem is inherently non-convex, making it difficult to solve numerically. Therefore, we reformulate the problem as a bi-level programming problem—the outer-level sub-problem addresses beamforming vectors using a genetic algorithm while the inner-level sub-problem deals with the allocation of downlink and uplink time through the Lagrange multiplier method. Numerical results show that the proposed hybrid NOMA-TDMA scheme outperforms baseline schemes like orthogonal multiple access and equal time allocation, in terms of the sum throughput of the WPIN.

In this paper, we propose deep learning–based energy beamforming in a multi-antennae wireless powered communication network (WPCN). We consider a WPCN where a hybrid access point (HAP) equipped with multiple antennae broadcasts an energy-bearing signal to wireless devices using energy beamforming. We investigate the joint optimization of the time allocation for wireless energy transfer (WET) and wireless information transfer (WIT) with the design for energy beams while minimizing the transmit power at the HAP for efficient use of its available resources. However, this is a non-convex problem, and it is numerically intractable to solve it. In the literature, the traditional approach to solving this problem is based on an iterative algorithm that incurs high computational and time complexity, which is not feasible for real-time applications. We study and analyze a deep neural network (DNN)-based scheme and propose a faster and more efficient approach for the fair approximation of a near-optimal solution to this problem. To train the proposed DNN, we acquire training data samples from a sequential parametric convex approximation (SPCA)-based iterative algorithm. Instead of acquiring data samples and training the DNN, which is highly complex, we use offline training for the DNN to provide a faster solution to the real-time resource allocation optimization problem. Through the simulation results, we show the proposed DNN scheme provides a fair approximation of the traditional SPCA method with low computational and time complexity.

The integration of wireless powered communication network (WPCNs) with intelligent reflecting surface (IRS) technology has emerged as a promising solution for enhancing the energy and spectral efficiency of the network. Recent studies have explored the benefits of active and passive reflecting element surfaces in various networks. However, most existing works on IRS-assisted WPCNs mainly focus on comparing an active IRS to single/multiple IRSs or to a hybrid IRS comprising active and passive elements on the same surface. In this paper, we explore a hybrid, active and passive IRS-assisted WPCN which can significantly improve network capacity. By introducing a distributed pair of active and passive IRSs, signal amplification and multiple reflection links can boost the wireless link capacity. To enhance network performance, this new framework utilizes both active and passive IRS elements to optimize wireless energy transfer (WET) on the downlink and wireless information transfer (WIT) on the uplink. However, designing a joint deployment of active and passive IRS elements becomes more challenging due to the joint operations of downlink WET and uplink WIT in WPCNs. To address the complexity of the non-convex optimization problem associated with the hybrid IRS-aided architecture, we use a systematic divide-and-conquer methodology. This decomposes the main problem into three interconnected sub-problems that are solved sequentially using sophisticated techniques. Furthermore, the study examines two practical deployment scenarios for active and passive IRS elements, namely ’Hybrid Case-1’ (where active IRS is placed near the hybrid access point (H-AP) and passive IRS is placed near wireless devices) and ’Hybrid Case-2’ (where active IRS is placed near wireless devices and passive IRS is placed near the H-AP). Extensive simulations and numerical analysis demonstrate the superiority of our proposed hybrid IRS-based framework over single active IRS and conventional double passive IRSs-assisted network, making it a promising and compelling solution for practical WPCN deployments, ensuring enhanced network performance and throughput fairness in wireless power and communications technologies.

In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.