You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: 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.
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 [2] 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 [2] is further extended to a more generic network setup with the energy from multiple nearby PBs taken into consideration in [1] . However, the work in [2] and [1] 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.