CSI-Free vs CSI-Based Multi-Antenna WET for Massive Low-Power Internet of Things

Abstract

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSIbased scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.

Massive Wireless Energy Transfer: Enabling Sustainable IoT Toward 6G Era

“…1) increasing the end-to-end system efficiency, while limiting further the energy consumption of EH devices [48]; 2) seamless network-wide integration of wireless communication and energy transfer (at all the system levels) [48]; 3) powering a massive number of devices, while enabling ubiquitous energy accessibility with QoS guarantees [49]- [51]. Several techniques and technological trends that seem suitable for enabling WET as an efficient and competitive solution for sustainably powering future IoT networks are discussed next.…”

“…The system parameters are given in Table III. Note that WET channels are usually under a strong line of sight (LOS) influence, thus, the Rician distribution is usually appropriate for fading modeling [36], [49]- [51], [59]. In this case, a LOS factor of 10 dB, i.e., 10 dB above the non-LOS components, is adopted [59].…”

Performance Analysis of ML-Based MTC Traffic Pattern Predictors

“…The MTC traffic is usually uplink-dominated and characterized by short transmissions combining real-time and non real-time traffic from multiple sources. According to its applications, MTC has three elementary traffic patterns [17]: (i) periodic update (PU), under which devices transmit status reports regularly, e.g., smart meter reading (gas, electricity, water); (ii) event-driven (ED), which describes non-periodic traffic due to a specific random trigger at an unknown time, e.g., alarms; and (iii) payload exchange (PE), which consists of bursty traffic that usually comes after PU or ED traffic.…”

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

“…Nevertheless, real world applications often combine these traffic types. Hence, considering the three elementary classes above enables building models with an arbitrary degree of complexity and accuracy [38]- [40].…”

“…First, we model the position of MTDs and event epicenters as distinct and independent Poisson point processes (PPPs), and define a function to model the influence of events on MTC traffic. It is noteworthy that the model is able to characterize the different traffic patterns described in the literature, e.g., [38]- [40]. We leverage a simple neural network (NN) to predict MTC traffic patterns and configure WuS accordingly.…”