Fig 1 - uploaded by Nirwan Ansari
Content may be subject to copyright.
Energy harvesting enabled wireless communication networks.
Source publication
As energy harvesting (EH) technologies advance, wireless networks will potentially and eminently be powered by harvested energy such that carbon footprints can be reduced. Challenged by the dynamic nature of green energy source availability, various methods have been proposed so that harvested energy can be hoarded for future use or transferred to...
Contexts in source publication
Context 1
... harvesting is empowering a new paradigm of wire- less communication networks (WCNs). Individual base sta- tions (BSs) in macro cells or small cells are now capable of be- coming energy providers. As illustrated in Fig. 1, either being connected to the green power farm through the power grid or having standalone green power generators, EH enabled macro BSs and low power nodes (LPNs) are readily constructed by telecommunications equipment manufacturers ...
Context 2
... η < 1 is the energy conversion efficiency which depends on the physical circuit of the EHD. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 As we can see, for the dedicated MCU/SCU or the separated receiver architecture shown in Fig. 2a, α = 1. Meanwhile, as far as the tradeoffs between data rate and harvested energy is concerned, power splitting achieves better performance than time switching ...
Citations
... In network optimization, ref. [14] proposed a method to minimize network delay through simplified energy management and conservation constraints for fixed data and energy routing topologies. Meanwhile, ref. [15] explored various energy-sharing mechanisms among multiple EH devices within the network. When data transmission is possible, but there is insufficient energy in the device battery, external energy supply from nearby secondary power sources must be considered. ...
... However, such simplified channel models fail to account for the dynamic communication environment, which is crucial for optimizing resource allocation and analyzing system performance. Alternative approaches, like energy scheduling and management methods, have been adopted in references [15][16][17][18], which assume a practical probability distribution model for energy arrival. However, these methods do not consider optimal power allocation, energy borrowing, and returning schedules for harvested energy relaying for IT. ...
Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of a wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy, by using energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex, as it involves non-convex variables and requires combinatorial relay selection indicator optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimization techniques would have difficulty solving in complex problem environments. The proposed algorithm significantly improved the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%, 27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.
... In network optimization, [14] proposed a method to minimize network delay through simplified energy management and conservation constraints for fixed data and energy routing topologies. Meanwhile, [15] explored various energy-sharing mechanisms among multiple EH devices within the network. When data transmission is possible, but there is insufficient energy in the device battery, external energy supply from nearby secondary power sources must be considered. ...
... However, such simplified channel models fail to account for the dynamic communication environment, which is crucial for optimizing resource allocation and analyzing system performance. Alternative approaches, like energy scheduling and management methods, have been adopted in references [15][16][17][18], which assume a practical probability distribution model for energy arrival. However, these methods do not consider optimal power allocation, energy borrowing, and returning schedules for harvested energy relaying for IT. ...
... for n = 1, 2, ..., N do 6: Obtain the current normalized action vector as a [n] = µ (s [n] |θ µ ) + CN (0, ǫ) 7: Obtain next state vector s [n + 1] by (1), (15), (16), (17) ...
Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.
... Eight GBSs are deployed in a 100 m × 100 m area to provide green energy wireless charging. The initial amount of available green energy at each GBS of every charging round is randomly generated between 2 and 15 Wh [40] and each GBS's wireless charging powering is set to 10 W. The wave-impedance Z 0 and wavelength λ j of an electromagnetic wave are 120pi and 12.5 cm, respectively. The antenna gain g j is 15 dBi. ...
With the worldwide ubiquitous implementation of Internet of Things (IoT), IoT devices (IoTDs) and their emerging applications are commendably enriching people’s daily life with intelligence and convenience. However, a tremendous number of IoTDs all over the world, incurring a huge amount of energy consumption, are exacerbating the global electric grid load and natural environment changes while the electronic equipment traditional charging approaches are unable to efficiently power the IoTDs. Leveraging green energy to remotely charge the IoTDs (i.e., green energy far-filed wireless charging) is the essential solution to revolutionarily resolve these problems. In this work, we propose a two-step green energy wireless charging (TREE) algorithm to efficiently power the IoTDs for the multiple-green base stations (GBSs)-to-multiple IoTDs charging scenarios. First, we propose the recharging threshold model for IoTDs and schedule the IoTDs to be efficiently charged by their associated GBSs within the green wireless charging time period. Second, for those IoTDs that will not be fully charged, we propose the multi-GBS joint accumulative charging scheme to fulfill most of the IoTDs’ charging requirements within the charging period. Finally, we validate the performance of the proposed algorithm through extensive simulations.
... The world can be computerized using IoT technology via internet connectivity that requires energy and power for its use. Therefore, sufficient energy needs to be furnished for the IoT devices to drive the network in a self-sustainable manner [17]. This technique can be used to withdraw the necessity of renewing and reinstating the batteries. ...
This paper studies how to efficiently harvest energy and improve network throughput so that the narrowband user equipment has a longer life span instead of replacing batteries every few years to keep them working. To harvest enough energy for spectrum sensing and data transmission, we propose a grouping-based network which also maximizes the network throughput. Initially, instead of sensing the spectrum, we first harvest the energy and then use this harvested energy for spectrum sensing and data transmission. The grouping is done in such a way that the users which are in closer proximity to the AP are considered a group. The devices which are out of the range of access point (AP) can harvest energy, but it is comparatively less than those devices which are grouped with AP. In such cases, they are grouped with the AP that is closer to them. The main aim is to escalate the amount of harvested energy and extend the life span of the devices to have as many successful data transmissions as possible. Since the devices use the spectrum allocated to primary users (Pu) for data transmissions and the devices can transmit data only when the PU’s spectrum is free, the proposed model is beneficial for wireless body area networks where electronic health monitoring is one of the major applications.
... The charging round T is 1 h [29], [30]. The initial amount of harvested green energy at each GBS is randomly generated between 2 and 15 Wh [40] and the wireless energy transmission power of each GBS is 10 W. The wave-impedance Z 0 is 120π and the wavelength λ j is 12.5 cm (i.e., wireless charging frequency is 2.4 GHz). The power gain g m,n between GBS n and IoTD m is 15 dBi and the transmission phase shift φ n is randomly generated between 0 and 2π . ...
Powering a huge number of Internet of Things devices (IoTDs), necessitated in many IoT applications, is a dreadful problem in many circumstances, in terms of the cost of labor, time and so on. Far-field green energy wireless charging is a promising technique to remotely power IoTDs in a large-scale network. In order to improve the end-to-end energy efficiency, for the scenario with multiple green base stations (GBSs) wirelessly charging multiple IoTDs, our previous research work proposed a wireless charging scheme to aggregate the wireless power received by a static IoTD from multiple GBSs. In reality, in various emerging IoT applications, a large proportion of IoTDs are mobile. Hence, in this work, we focus on intelligently assigning multiple GBSs to wirelessly charge mobile IoTDs such that the IoTDs can be efficiently powered. We first propose the moving model and the charging model of IoTDs. Based on the non-linear wireless charging model and non-linear wireless energy conversion model, we then propose the Greedy dynAmic joInt chargiNg (GAIN) algorithm to efficiently power the mobile IoTDs. Through extensive simulations, we validate the performance of the proposed algorithm.
... This interrupts the signal transmission thus UAV assisted relaying helps to facilitate successful transmission. RF-EH plays a crucial role to fulfil the power requirement demand [11] of these aerial base stations. Motivated by the benefits of AF relay which offers low complexity design at the transceiver and has an adaptive approach for different modulation schemes. ...
Recently wireless powered networks have emerged as cutting-edge technology for addressing the power constraint issue of wireless devices (WD’s). This technology enables wireless nodes to harness power from the ambient radio frequency (RF) signal thus enhances the energy efficiency of the communication network and also improves the network longevity. The underlying principle of energy harvesting (EH) by wireless power transfer (WPT) has implications on system performance due to link distance and channel fading. To address the impact of channel fading on energy constraints WD’s this work explores the maximal ratio combining (MRC) diversity at the receiver node for the presented simultaneous wireless information and power transfer (SWIPT) model considering the energy constraint unmanned aerial vehicle (UAV) mounted amplify and forward (AF) relay. Assuming fluctuating two ray (FTR) fading scenario a novel analytical expression for the outage probability (OP) and symbol error rate (SER) for the presented system has been derived. As the FTR fading channel provides a generalized fading model and can significantly model millimeter wave band signals. Based on derived performance metrics this paper investigates the impact of variation on node positioning and EH time allocation factor on system outage probability (OP) and symbol error rate (SER) performance. Finally, the derived expression has been validated by comparing the results obtained from the Monte Carlo simulation.
... In other words, the energy surplus and short fall of BSs can average out by exchanging the energy among BSs. The energy sharing mechanism can be classified as shown in Table VII [131]. In contrast with direct energy sharing, traffic offloading, which gives more traffic load to BSs with surplus energy, is regarded as non-direct energy sharing. ...
... SHARING CLASSIFICATION[131]. ...
div>
Renewable energy (RE)-powered base stations (BSs) have been considered as an attractive solution to address the exponential increasing energy demand in cellular networks while decreasing carbon dioxide (CO2) emissions. For the regions where reliable power grids are insufficient and infeasible to deploy, such as aerial platforms and harsh environments, RE has been an alternative power source for BSs. In this survey paper, we provide an overview of RE-enabled cellular networks, detailing their analysis, classification, and related works. First, we introduce the key components of RE-powered BSs along with their frequently adopted models. Second, we analyze the proposed strategies and design issues for RE-powered BSs that can be incorporated into cellular networks and categorize them into several groups to provide a good grasp. Third, we introduce feasibility studies on RE-powered BSs based on the recent literature. Fourth, we investigate RE-powered network components other than terrestrial BSs to address potential issues regarding RE-enabled networks. Finally, we suggest future research directions and conclusions.
</div
... In other words, the energy surplus and short fall of BSs can average out by exchanging the energy among BSs. The energy sharing mechanism can be classified as shown in Table VII [131]. In contrast with direct energy sharing, traffic offloading, which gives more traffic load to BSs with surplus energy, is regarded as non-direct energy sharing. ...
... SHARING CLASSIFICATION[131]. ...
div>
Renewable energy (RE)-powered base stations (BSs) have been considered as an attractive solution to address the exponential increasing energy demand in cellular networks while decreasing carbon dioxide (CO2) emissions. For the regions where reliable power grids are insufficient and infeasible to deploy, such as aerial platforms and harsh environments, RE has been an alternative power source for BSs. In this survey paper, we provide an overview of RE-enabled cellular networks, detailing their analysis, classification, and related works. First, we introduce the key components of RE-powered BSs along with their frequently adopted models. Second, we analyze the proposed strategies and design issues for RE-powered BSs that can be incorporated into cellular networks and categorize them into several groups to provide a good grasp. Third, we introduce feasibility studies on RE-powered BSs based on the recent literature. Fourth, we investigate RE-powered network components other than terrestrial BSs to address potential issues regarding RE-enabled networks. Finally, we suggest future research directions and conclusions.
</div
... These strategies respond to the smart-grid requests by switching off the resources at the BS to lower the energy demand and take part in DR policy. Gambín et al., (2019) Power packet grid Ahmed et al., (2019) Non-convex nonlinear energy sharing Hybrid energy sharing framework Cost aware energy cooperation Huang et al., (2017), Huang and Ansari (2015) Wired and wireless energy transfer Farooq et al. (2016a) Agglomerative and divisive clustering Jahid et al. (2016) Energy cooperation exploiting tempo-spatial iversities Chia et al. (2014b) Offline and online energy cooperation Aggregator Du et al. (2019) Distributed online energy management Pawar et al. (2018) Demand and energy generation mismatch minimization Sheng et al. (2016) Load shifting and energy sharing Xu et al. (2015) Energy and communication sharing Guo et al. (2014b) Joint energy and spectrum sharing Smart Grid Piovesan et al. (2019a) Traffic and load control Oikonomakou et al. (2019) Energy sharing and trading in HetNets Thakur et al. (2017) Cost and energy aware cell selection Reyhanian et al. (2017) Centralized and decentralized energy trading Xu et al. (2017) Online energy cooperation Farooq et al. (2016a) Agglomerative and divisive clustering Farooq et al. (2016b) Stochastic and linear programming Ramamonjison and Bhargava (2016) Efficient resource and energy allocation Ahmed et al. (2016), Leithon et al. (2014b) Cost reduction policy Reyhanian et al. (2015) Online centralized approach ...
A massive increase in the amount of data traffic over mobile wireless communication has been observed in recent years, while further rapid growth is expected in the years ahead. The current fourth-generation (4G) mobile networks are evolving to the fifth-generation (5G) networks to fulfill the demand for high data rates and broad network coverage. The advent of the ultra-dense 5G network and a vast number of connected devices will bring about the obvious issues of significantly increased system energy consumption, operational expenses, and carbon dioxide emissions. Renewable energy is considered a viable and practical approach to power the small cell base station in an ultra-dense 5G network infrastructure to reduce the energy provisions from the electric grid and carbon dioxide emissions. In this paper, we discuss the role of renewable energy in the design of sustainable, eco-friendly, and cost-effective 5G mobile networks and provide a comprehensive survey on the state-of-art of renewable energy management techniques aiming to promote the sustainability and cost reduction of the large-scale mobile wireless infrastructures. This survey specifically covers a variety of energy efficiency techniques, the utilization of renewable energy sources, interaction with the smart grid (SG), and the renewable energy powered base stations. It also highlights the outstanding technical challenges and future perspectives to enable future sustainable 5G network infrastructure.
... They coordinate the energy and information transmissions between HAPs and EH devices. Moreover, these HAPs may be solar powered as network operators aim to reduce the carbon footprint or operating expenditure of their network [7]. Consequently, a large number of HAPs can be deployed without any reliance on mains electricity [8]. Figure 1 shows an example where two HAPs are responsible for charging two sensor devices. ...
Future Internet of Things networks are likely to have radio frequency (RF) energy harvesting sensor devices and also solar-powered hybrid access points (HAPs) that help monitor one or more targets such as assets in a warehouse. In this setting, we outline three contributions that ensure devices’ monitor targets at all times and upload the maximum number of samples to an HAP in order to ensure high monitoring quality. First, we outline a mixed integer linear program (MILP) and use it to optimize the transmission power of HAPs and time used by devices to harvest RF energy, monitor target(s), and data transmissions. Second, we propose two heuristic algorithms to solve large problem instances. The results show that our heuristics achieve almost 97% of the optimal result computed by the MILP.
