Energy Cooperation in Battery-Free Wireless Communications with Radio Frequency Energy Harvesting

Radio frequency (RF) energy harvesting techniques are becoming a potential method to power battery-free wireless networks. In RF energy harvesting communications, energy cooperation enables shaping and optimization of the energy arrivals at the energy-receiving node to improve the overall system performance. In this paper, we proposed an energy cooperation scheme that enables energy cooperation in battery-free wireless networks with RF harvesting. We first study the battery-free wireless network with RF energy harvesting then state the problem that optimizing the system performance with limited harvesting energy through new energy cooperation protocol. Finally, from the extensive simulation results, our energy cooperation protocol performs better than the original battery-free wireless network solution.特

Toward End-to-End, Full-Stack 6G Terahertz Networks

Recent evolutions in semiconductors have brought the terahertz band in the spotlight as an enabler for terabit-per-second communications in 6G networks. Most of the research so far, however, has focused on understanding the physics of terahertz devices, circuitry and propagation, and on studying physical layer solutions. However, integrating this technology in complex mobile networks requires a proper design of the full communication stack, to address link- and system-level challenges related to network setup, management, coordination, energy efficiency, and end-to-end connectivity. This paper provides an overview of the issues that need to be overcome to introduce the terahertz spectrum in mobile networks, from a MAC, network and transport layer perspective, with considerations on the performance of end-to-end data flows on terahertz connections.Comment: Published on IEEE Communications Magazine, THz Communications: A Catalyst for the Wireless Future, 7 pages, 6 figure

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

Full Duplex Spectrum Sensing and Energy Harvesting in Cognitive Radio Networks

النطاق المزدوج (FD) للطيف الترددي في شبكات الاتصالات اللاسلكية للراديو الادراكي مع حصاد الطاقة (Full-duplex Energy Harvesting Cognitive Radio Networks “FD EHCRNs”)، والتي هي عبارة عن مزيج من تقنية الإرسال المزدوج الكامل (FD) الراديو الادراكي (CRN) وتقنية حصاد الطاقة (EH)، هي تقنية اتصالات لاسلكية جديدة الغرض منها تحسين كفاءة الطيف وتحسين كفاءة الطاقة. باستخدام النطاق المزدوج (FD) للطيف يمكن لأجهزة الراديو الادراكي عمل تحسس واستشعار متزامن لطيف الشبكات الأخرى التي يرغب في استخدام النطاق الترددي الغير مستخدم فيها وعمل نقل البيانات عبر هذا النطاق وكذلك عمل حصاد للطاقة بشكل متزامن في نفس الوقت، لذلك يمكن لنظام النطاق المزدوج في EH CRNs حل مشاكل الطيف المتقطع المتواجدة في شبكات CRN التقليدية. في هذه البحث تم تقديم اقتراح نموذج جديد من FD EHCRN من خلال التركيز على تصميم حدود الكشف (detection thresholds) ونموذج تجميع الطاقة (energy harvesting) وذلك لغرض تحسين أداء النظام الراديو الادراكي مع تحصيل الطاقة باستخدام النطاق المزدوج. مع العلم أن هذا البحث لا يسعى إلى تصميم تقنية جديدة لاستشعار الطيف من أجل EH-CRN بل إعادة تصميم واقتراح نموذج جديد لتقنية استشعار الطيف من خلال استخدام النطاق المزدوج باستخدام هوائيين. حيث تم عرض كل من التحليل الرياضي والنتائج العددية في هذا البحث.Full-duplex Energy Harvesting Cognitive Radio Networks (FD EHCRNs), which is a combination of full-duplex (FD) technique, cognitive radio (CR), and radio frequency (RF) energy harvesting technique, is a new wireless communication model to improve spectrum efficiency (SE) and energy efficiency (EE). Using FD, the Energy Harvesting Cognitive Radio Networks (EH CRN) equipment of the cognitive users can perform spectrum sensing, data transmission, and energy harvesting simultaneously. Consequently, full duplex in EH CRNs can solve the spectrum waste and transmission discontinuation problems caused by traditional CRNs. In this paper, a new proposal model for FD EHCRN is presented focusing on detection threshold design and energy harvesting model to try improving the system performance. Therefore, the purpose of this paper is to redesign the existing EHCRN and proposes a new model for spectrum sensing technique using full-duplex with only two antennas. Both mathematical analysis and numerical results are presented in this paper

Optimized Training Design for Wireless Energy Transfer

Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems.Comment: 30 pages, 9 figures, to appear in IEEE Trans. on Communication