In the fifth generation era, the pervasive applications of Internet of Things and massive machine-type communications have initiated increasing research interests on the backscatter wireless powered communication (B-WPC) technique due to its ultra high energy efficiency and low cost. The ubiquitous B-WPC network is characterized by nodes with dynamic spatial positions and sporadic short packets, of which the performance has not been fully investigated. In this paper, we give a comprehensive analysis of a multi-antenna B-WPC network with sporadic short packets under a stochastic geometry framework. By exploiting a time-space Poisson point process model, the behavior of the network is well captured in a decentralized and asynchronous transmission way. We then analyze the energy and information outage performance in the energy harvest and backscatter modulation phases of the backscatter network, respectively. The optimal transmission slot length and division are obtained by maximizing the network-wide spatial throughput. Moreover, we find an interesting result that there exists the optimal tradeoff between the durations of the energy harvest and backscatter modulation phases for spatial throughput maximization. Numerical results are demonstrated to verify our analytical findings and show that this tradeoff region gets shrunk when the outage constraints become more stringent.
Despite the above-mentioned prominent applications in IoT, the area of the large-scale B-WPC network with multiple reader-tag links has been largely uncharted. A very recent contribution @cite_32 considers the network coverage probability and transmission capacity of a B-WPC network with power beacons (PBs), where the network is modeled as a random Poisson cluster process by leveraging stochastic geometry. Most recently, the network model in @cite_32 is further extended to a more generic network setup with the energy from multiple nearby PBs taken into consideration in @cite_43 . However, the work in @cite_32 and @cite_43 only focuses on the scenario where the energy is harvested from dedicated power beacons, while the performance of the decentralized B-WPC MANET remains unknown. Most importantly, all the above work fails to capture the nature of the B-WPC network in pervasive IoT and mMTC applications with asynchronous transmissions and the incurred time-varying interference. The above reasons motivate our work.
@cite_43 @cite_32
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Powering the massive number of small computing devices in the Internet of Things (IoT) is a major challenge because replacing their batteries or powering them with wires is very expensive and impractical. A viable option to enable a perpetual network operation is through the use of far-field Wireless Power Transfer (WPT). We study a large network architecture that uses a combination of WPT and backscatter communication. The network consists of power beacons (PBs) and passive backscatter nodes (BNs), and their locations are modeled as points of independent Poisson point processes (PPPs). The PBs transmit a sinusoidal continuous wave (CW) and the BNs reflect back a portion of this signal while harvesting the remaining part. A BN harvests energy from multiple nearby PBs and modulates its information bits on the composite CW through backscatter modulation. The analysis poses real challenges due to the double fading channel, and its dependence on the PPPs of both BNs and PBs. With the help of stochastic geometry, we derive the coverage probability and the capacity of the network in tractable and easily computable expressions. These expressions depend on the density of both PB and BN, both forward and backward path loss exponents, transmit power of the PB, backscattering efficiency, and number of PBs in a harvesting region. We observe that the coverage probability decreases with an increase in the density of the BNs, while the capacity of the network improves. We compare the performance of this network with a regular powered network in which the BNs have a reliable power source and show that for certain parameters the coverage of the former network approaches that of the regular powered network. Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering the massive number of embedded devices is a key challenge of designing IoT, since batteries increase the devices’ form factors and battery recharging replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In this network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle, reflection coefficient, and the PB density. The effects of the parameters on network performance are quantified. In this paper, we investigate schemes for energy-efficient multi-hop broadcasting in large-scale dense Wireless Sensor Networks. We begin with an initial simplified study of the schemes for relay selection. Our first finding is that MPR-based (Multipoint Relay) mechanisms work poorly in a dense network while the recently proposed Multicast Protocol for Low power and Lossy Networks (MPL) protocol based on Trickle performs better. However, Trickle requires to overhear packet retransmissions in the vicinity, while sensor nodes try to avoid overhearing by periodically waking up and going to sleep to save energy. We propose Beacon-based Forwarding Tree (BFT), a new scheme that achieves similar performance to MPL, although it fits better the case of nodes with low radio duty cycling MACs of the type of beacon-enabled IEEE 802.15.4. Our scheme also guarantees network coverage and its optimized version results in the shortest path distance to the broadcast source at a cost of lesser load mitigation. We compare and discuss the measured performance of MPL on top of ContikiMAC and BFT over beacon-enabled 802.15.4 on a Contiki testbed. The experimental results of the comparisons show that BFT may achieve very good performance for a range of broadcast intensity, it has a predictable power consumption, a remarkable low power consumption for leaf nodes, and low loss rates. On the other hand, MPL over ContikiMAC can obtain very low duty cycles for low broadcast traffic. The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS’ power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.