Wireless Information and Power Transfer: A New Paradigm for Green Communications

The first effort to transmit energy wirelessly with the purpose of doing so is attributed to N. Tesla at his laboratory in Long Island, New York, USA. Then, about 30 years after J. Maxwell had demonstrated the potentials in 1873 the conveyance of energy trough vacuum via electromagnetic waves, corroborated in principle 15 years later by H. Hertz. The current expansion of the WPT via radio frequency beam owes to William Brown in 1960s using microwave technology developed during the World War II. Wireless Power Transfer (WPT) is gaining traction in many application domains because it offers the possibility of batteryless operation and wireless charging. Although wireless charging frequently gets the attention of the media, batteryless operation can bring major benefits for the environment and the massive deployment of wireless sensors in the Internet of Things (IoT). The salient feature of harvesting energy from electromagnetic radiation allows to gather energy even from ambient sources. Interference exploitation can also form a useful recourse at the expense of quality of experience. This chapter provides an overview of simultaneous wireless information and power transfer (SWIPT) systems with a particular focus on emerging techniques associated with SWIPT. We explore various key design issues in the development of SWIPT assisted emerging wireless communications technologies including the ones related to 5G communications. Chapter also provides interesting future research ideas and directions for interesting researchers.

Full-duplex (FD) radios that can simultaneously transmit and receive on the same frequency channel have emerged as a solution to improve the spectral efficiency. At the same time, wireless power transfer (WPT) techniques have been advocated as alternative methods to power the wireless networks with reduced carbon footprint. In this chapter, we overview the state-of-the-art advances in FD communications and WPT. Specifically, FD wireless-powered network design with examples from (a) bi-directional topology, (b) relay topology, and (c) hybrid access point topology is presented. In particular, in each topology we analyze the system performance and consider beamforming design to optimize the performance. In addition, the impact of beamforming choice and various system/energy harvesting parameters on the performance are discussed in detail. Finally, future research directions and open problems associated with FD and WPT are pointed out.

In this chapter we consider time switching (TS) simultaneous wireless information and power transfer (SWIPT) for a small-cell network consisting of multiple multi-antenna access points (APs) that serve multiple single antenna user equipments (UEs). In this scenario, we address the jointly optimized design of spatial precoding and TS ratios under a multi-objective optimization (MOO) framework. Our goal is to maximize a utility vector including the data rates and harvested energies of all UEs simultaneously. This problem is a non-convex rank-constrained MOO problem which is transformed into a non-convex semidefinite program using the weighted Chebyshev method. We propose an algorithm based on majorization–minimization approach to solve this problem. Numerical results are provided to demonstrate the performance of the proposed beamforming and TS algorithm in terms of harvested energy-data rate trade-off. The effect of different parameters on this trade-off is also investigated to get a general overview for the practical design of the network.

This chapter focuses on the latest developments in the area of interference exploitation for harvesting useful signal power from constructive interference. This complements existing work on energy harvesting and wireless power transfer, where wireless interference acts not only as a source of RF energy for powering the transceiver hardware, but also as a source of useful signal energy for enhancing signal detection. Starting with an overview and motivation for exploiting interference, this chapter sets out the fundamental definitions and mathematical interference characterization criteria. It then provides a broad overview of the existing techniques for harvesting useful signal power from interference: from closed-form linear and non-linear precoders, to recent beamforming optimizations and resource allocation. Moving a step further, this chapter overviews the state of the art in the area of RF energy harvesting with interference exploitation, where constructive interference is harvested both as useful signal power and as an RF energy source. The discussion concludes with an overview of open problems in the area of interference exploitation.

Energy harvesting is a promising solution to the limited lifetime of battery-operated devices in wireless relaying systems. In this chapter, different aspects of energy harvesting relaying will be studied. First, the performances of energy harvesting relaying systems with or without interference will be analysed in terms of different performance measures. This analysis helps to show the benefit of energy harvesting relaying. Then, a new energy harvesting relaying protocol that allows the source node to harvest energy from the relay node during the relaying phase will be examined. The extra energy harvested by the source will improve the system performance further. Finally, the channel estimation problem for energy harvesting relaying will be discussed, where the pilots used in channel estimation are transmitted using the harvested energy.

The energy efficiency of systems in general determines its operational sustainability. Harvesting energy is a crucial technology for a variety of wireless systems that have limited access to a reliable electricity supply or recharging sources. As such, these devices need to harvest electricity from alternative sources such as the natural environment or wireless signals. A variety of wireless systems and devices fit this profile, from relatively power-hungry macro-base stations deployed in remote regions, to nano-scale sensors in vivo environments. The wide range of devices transverse multiple length scales and communicate across distance scales that vary by 9 orders of magnitude (from km to microns). It remains unclear what set of energy harvesting technologies are suitable. This chapter will review state-of-the-art technologies that allow multi-scale wireless devices to simultaneous harvest energy and transmit data.

Radio frequency (RF) energy harvesting is the process by which radiative electro-magnetic waves, typically from 3 kHz to 300 GHz, are captured, converted, stored and used to operate usually low-energy consumption devices ranging from wearable electronics to sensor networks. RF signals can be primarily generated from two sources: dedicated and ambient RF sources. The former can be deployed to provide energy when predictable supply is expected, usually from license-free frequency bands of radio spectrum. The latter refer to RF transmitters originally not intended for energy transfer, such as TV towers, radio towers, and Wi-Fi routers. Until recently, most implementations of networks powered by RF energy were demonstrated through experiments and prototype setups as proof-of-principles and to show potential practical applications. Now, with the ubiquitousness of Wi-Fi and the advance in CMOS technology and supercapacitors, RF-powered devices have found various applications and are now used or offered in commercial products. The aim of this book chapter is to provide a basic understanding of RF energy harvesting (RFEH) techniques, describe the underlying electronics hardware design, show the current state-of-the-art applications and commercially available products, and finally expand to future applications and challenges ahead.

The future Internet of Things (IoT) will connect trillions of devices, where wireless sensors will play an important part. Due to the large scale of such networks, battery replacement is a crucial issue for the massive number of wireless sensors. To efficiently address the finite sensor lifetime problem in IoT, techniques such as energy harvesting powered and wireless power transfer powered WSNs are promising solutions. In this chapter, first, we summarize the state-of-the-art WSNs, EH-based WSNs, and wireless power transfer techniques, and then motivate wireless power transfer-based WSNs. Also we present the major design challenges for wireless power transfer-based status monitoring WSNs, including accurate modeling of sensor energy costs and metrics to take into account the age of the sensed information. We present a novel solution to one of the challenges. Specifically, we present a harvest-then-use protocol and consider two complementary performance metrics to measure the timeliness of the status monitoring WSN, i.e., update cycle and update age. Moreover, we present a framework of analysis for both the update cycle and the update age, which takes into account both the energy cost of sensing and transmission.

The vision of creating a sustainable and smart planet has encouraged researchers to move towards more energy efficient and user-friendly technologies giving rise to the notion of 5G standard. 5G standard promises to be a fusion of emerging wireless technologies like heterogeneous networks, software defined networks, device-to-device communication, just to name a few. It is envisioned to bring with itself the ability to alleviate the problems of spectrum under-utilization and energy inefficiency. Earlier the areas of spectrum and energy management were taken into account separately, however, in this chapter we have tried to analyze these areas jointly. This chapter proposes a simultaneous approach of using cooperative spectrum sharing and RF energy harvesting techniques for enhancing the quality of service (QoS) of next-generation wireless network.

Different to those works, we use a model which arises naturally from fundamental properties of the superposition of energy fields. This model has been shown to be more realistic than other one-dimensional models that have been used in the past and can capture superadditive and cancellation effects. Under this model, we define two new interesting problems for configuring the wireless power transmitters so as to maximize the total power in the system and we prove that the first problem can be solved in polynomial time. We present a distributed solution that runs in pseudopolynomial time and uses various knowledge levels and we provide theoretical performance guarantees. Finally, we design three heuristics for the second problem and evaluate them experimentally.

Network operators exploit temporal and spatial fluctuations in call traffic load to save energy by switching off their lightly loaded base stations (BSs), a technique that is referred to as dynamic planning. Research efforts in the literature focus on satisfying the mobile users’ service quality in the downlink while implementing dynamic planning. However, the impact of dynamic planning on the service quality of uplink users is overlooked. In this context, switching off a nearby femto BS and associating uplink mobile users to a faraway macro BS might lead to service quality degradation due to longer transmission distance, and hence, larger transmission power. In turn, this could lead to violation of the minimum required uplink throughput and in severe cases this could result in mobile terminal battery depletion, and hence, call dropping. In this chapter, we quantify the impact of dynamic planning on mobile users’ service quality for data calls in both uplink and downlink. Then, we propose dynamic planning frameworks with balanced energy efficiency that account for the service quality of mobile users both at the downlink and uplink based on a two timescale decision problem. Simulation results demonstrate that the balanced dynamic planning frameworks can save energy for the network operators and provide service quality guarantee for mobile users in the downlink and the uplink as compared with an unbalanced (traditional) dynamic planning benchmark.