Weidang Lu

Collaborative Energy and Information Transfer in Green Wireless Sensor Networks for Smart Cities

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

Simultaneous Wireless Information and Power Transfer Based on Joint Subcarrier and Power Allocation in OFDM Systems

Energy harvesting (EH) is a prominent method to extend the operation time of energy-limited wireless networks. By integrating EH into wireless communications, the same spectrum is able to be used by simultaneous wireless information and power transfer (SWIPT) without affecting the quality of service. In this paper, we propose a joint subcarrier and power allocation-based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A fraction of the received subcarriers are allocated to form one group, which is used for information decoding (ID), and the remaining subcarriers form the other group, which is used for energy harvesting. Thus, no splitter is needed at the receiver. A joint subcarrier and power allocation problem is formulated to maximize the harvested energy, subject to the ID constraint. By using the dual decomposition method, an efficient algorithm is proposed to solve this joint resource allocation problem.

Subcarrier allocation based Simultaneous Wireless Information and Power Transfer algorithm in 5G cooperative OFDM communication systems

Abstract Self-sustainable communications are highly vital for large amounts of mobile terminals in the Fifth Generation (5G) communication systems. Simultaneous Wireless Information and Power Transfer (SWIPT) makes it possible that terminal transfers information while prolonging battery life by harvesting Radio Frequency (RF) energy. Though the traditional sub-carrier allocation based SWIPT algorithm in OFDM communication systems can optimize resource allocation, the receiver often cannot achieve higher information decoding rate when the channel condition of direct transmission deteriorates. In view of this situation, a sub-carrier allocation based SWIPT algorithm in 5G cooperative OFDM communication systems is proposed in this paper. The amplify-and-forward protocol is adopted by relay node which transmits information from source node to destination node by using a part of its sub-carriers. The remaining sub-carriers are used for energy harvesting. On the premise of minimum threshold of harvested energy, the sub-carrier and power allocations at relay node are optimized by establishing and solving the corresponding optimization model. Simulation results reveal that the proposed algorithm not only achieves the optimal sub-carrier and power allocations, but also improves information decoding rate with fast converging speed.

Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay

Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN’s achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

Joint resource optimization for DF relaying SWIPT based cognitive sensor networks

Abstract Spectrum and energy are two important resources for wireless sensor networks (WSNs). Cognitive Radio (CR) can improve the efficiency of spectrum utilization by allowing secondary users (SUs) access to the licensed bands as long as they do not significantly affect the performance of primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) can harvest energy and decode information simultaneously from radio-frequency (RF) signal, which can prolong the life time of energy-constraint networks (e.g., WSNs). In this paper, we propose a joint resource optimization scheme in respect of transmission power and power splitting ratio for decode-and-forward (DF) relaying SWIPT based cognitive sensor networks (CSNs), where relay sensor node (RSN) harvests energy from the signal of source sensor node (SSN) by using power splitting (PS) receiver architecture and utilize the harvested energy to help forward SSNs information. We maximize the throughput of SUs while guaranteeing the interference to PUs caused by SUs is smaller than the interference threshold. Simulation results demonstrate the performance of the joint resource optimization scheme.

Optimal Power Splitting of Cognitive Radio Networks with SWIPT-Enabled Relay

Cognitive radio (CR), as an intelligent spectrum sharing technology, can improve utilization of spectrum by sharing the licensed spectrum bands with secondary users (SUs) as long as do not have harmful effect on primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) combines wireless information transmission (WIT) technology and wireless power transfer (WPT) technology, which harvesting energy from ambient RF signals. In this paper, we consider amplify-and-forward (AF) cognitive radio networks (CRNs) with SWIPT-enabled secondary relay node. We aim to maximize the throughput of secondary network in considering the interference caused by the transmitted signal of secondary relay node to PUs, and derived the closed-form expression of the optimal power splitting ratio. Simulation results demonstrate the performance of the optimal power splitting ratio.

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

Channel Estimation in Next Generation LEO Satellite Communicastion Systems

Low earth orbit (LEO) satellite communication systems are the key parts of Space-Air-Ground networks. In order to deal with the scarcity of spectrum source, generalized frequency division multiplexing (GFDM) becomes a candidate for next generation LEO satellite systems. In LEO satellite communication systems, channel estimation is an indispensable technique to adapt to complex satellite channel environment. Because of the non-orthogonality between GFDM subcarriers, conventional channel estimation techniques can’t achieve the desired performance. We propose a Turbo receiver channel estimation method with threshold control to improve the channel estimation performance by utilizing the feedback information from Turbo decoder. The numerical and analytical results show that the proposed method can achieve better performance over LEO satellite channel.

Joint Resource Allocation for Wireless Energy Harvesting Based on DF Relaying

In this paper, a spectrum sharing protocol of cognitive radio (CR) based on joint resource allocation is proposed. The problem of spectrum access for one-way relaying ( CR ) networks using decode-and-forward (DF) relaying protocols is investigated. Specifically, in the first phase, the primary transmitter (( PT )) sends its own signal to primary receiver (( PR )) and cognitive transmitter (( CT )). Then, ( CT ) divides the received signal into two portions, which are used to decode information and harvest energy, respectively. In the second phase, the accessed bandwidth of ( CT ) is divided into two parts. One of the bandwidth is used to forward ( PT^{} s ) signal to ( PR ) with the harvested energy. ( CT ) can use the other of bandwidth to send ( CT^{} s ) signal to the cognitive receiver (( CR )) by using its own energy. The main object is to maximize cognitive system transmission rate by jointly optimizing the power splitting ratio and bandwidth allocation while satisfying the constraint of primary transmission rate.

Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network

The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models.