status: Published online

Multiple low power wide area networks (LPWANs) have been rolled out to support the variety of IoT applications. These networks vary largely in terms of quality-of-service, throughput and energy-efficiency. To cover all LPWAN use-cases most optimally, multiple networks can be combined into a multiple radio access technology (multi-RAT) solution. In particular environmental monitoring in both smart city and remote landscapes. We present and share such a multi-RAT platform. To derive an accurate profile of the multi-RAT opportunities in various scenarios, in the-field network parameter are monitored. The platform collects per-packet energy-consumption, packet delivery ratio (PDR) and other parameters of LoRaWAN, NB-IoT and Sigfox. Our preliminary measurements demonstrate the validity of using a multi-RAT solution. For example, we illustrate the potential energy savings when adopting multi-RAT in various scenarios.

The number of wireless devices is drastically increasing, resulting in many devices contending for radio resources. In this work, we present an algorithm to detect active devices for unsourced random access, i.e., the devices are uncoordinated. The devices use a unique, but non-orthogonal preamble, known to the network, prior to sending the payload data. They do not employ any carrier sensing technique and blindly transmit the preamble and data. To detect the active users, we exploit partial channel state information (CSI), which could have been obtained through a previous channel estimate. For static devices, e.g., Internet of Things nodes, it is shown that CSI is less time-variant than assumed in many theoretical works. The presented iterative algorithm uses a maximum likelihood approach to estimate both the activity and a potential phase offset of each known device. The convergence of the proposed algorithm is evaluated. The performance in terms of probability of miss detection and false alarm is assessed for different qualities of partial CSI and different signal-to-noise ratio.

Recent works have focused on utilizing LoRa to establish long-range and low-power connections between internet of things (IoT) nodes and gateways. A multi-hop scheme opens opportunities to greatly increase coverage in situations with adverse propagation effects or thin gateway deployment. In this work, we propose a multi-hop LoRa-based protocol tailored to battery-powered IoT devices. The protocol uses a combination of preamble sampling and dynamic payload aggregation to improve energy efficiency. In contrast to prior work, a fine-grained energy model of the devices is considered, impacting the design choices in all protocol layers, i.e., application, medium access control (MAC), and routing layer. The protocol is implemented in C and made public in open source. A first experiment indicates a high packet delivery ratio and showcases the dynamic aggregation feature.

Large array systems use a massive number of antenna elements and clever precoder designs to achieve an array gain at the user location. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs), to avoid distortion. This reduces the energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders can induce a coherent combining of distortion at the user locations, degrading the signal quality. In this work, novel linear precoders, simple to compute and to implement, are proposed that allow working close to saturation, while cancelling the third-order nonlinearity of the PA without prior knowledge of the signal statistics and PA model. Their design consists in saturating a single or a few antennas on purpose together with an negative gain with respect to all other antennas to compensate for the overall nonlinear distortion at the user location. The performance gains of the designs are significant for PAs working close to saturation, as compared to maximum ratio transmission (MRT) precoding and perfect per-antenna digital pre-distortion (DPD) compensation.

Autonomy of devices is a major challenge in many Internet of Things (IoT) applications, in particular when the nodes are deployed remotely or difficult to assess places. In this paper we present an approach to provide energy to these devices by Unmanned Aerial Vehicles (UAVs). Therefore, the two major challenges, finding and charging the node are presented. We propose a model to give the energy constrained node an unlimited autonomy by taking the Wireless Power Transfer (WPT) link and battery capacity into account. Selecting the most suitable battery technology allows a reduction in battery capacity and waste. Moreover, an upgrade of existing IoT nodes is feasible with a limited impact on the design and form factor.

Large array-based transmission uses the combination of a massive number of antenna elements and clever precoder designs to achieve array gain and spatially multiplex different users. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs). However, this reduces energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders, such as maximum ratio transmission (MRT), induces a coherent combining of distortion at the user location, degrading the signal quality. In this work, a linear precoder is proposed that allows working close to saturation while canceling the coherent combining of the third order nonlinear PA distortion at the user location. In contrast to other solutions, the zero third-order distortion (Z3RO) precoder does not require prior knowledge of the signal statistics and the PA model. The design consists of saturating a single or a few antennas on purpose together with an opposite phase shift to compensate for the distortion of all other antennas. The resulting array gain penalty becomes negligible as the number of base station antennas grows large.

In multiple input multiple output (MIMO) systems, precoding allows the base station to spatially focus and multiplex signals towards each user. However, distortion introduced by power amplifier nonlinearities coherently combines in the same spatial directions when using a conventional precoder such as maximum ratio transmission (MRT). This can strongly limit the user performance and moreover create unauthorized out-of-band (OOB) emissions. In order to overcome this problem, the zero third-order distortion (Z3RO) precoder was recently introduced. This precoder constraints the third-order distortion at the user location to be zero. In this work, the performance of the Z3RO precoder is validated based on real-world channel measurement data. The results illustrate the reduction in distortion power at the UE locations: an average distortion reduction of 6.03 dB in the worst-case single-user scenario and 3.54 dB in the 2-user case at a back-off rate of -3dB.

Massive Multiple Input Multiple Output (MIMO) offers superior capacity for future networks. In the quest for energy efficient implementation of these large array-based transmission systems, the power consumption of the Power Amplifiers (PAs) is a main bottleneck. This paper investigates whether it is possible to operate the PAs in their efficient nonlinear region, as the Out-of-Band (OOB) distortion may not get the same array gain as the in-band (IB) signals. We present a framework to simulate the effects under realistic conditions, leveraging on an accurate Ray-Tracing Simulator (RTS). The results show that the often assumed i.i.d. Rayleigh fading channel model results in too optimistic predictions, also in Non Line of Sight (NLoS) multi-path scenarios, regarding the spatial distribution of OOB emissions. We further comment on the consequences in view of current regulatory constraints.

A distributed antenna system (DAS) consists of several interconnected access points (APs) which are distributed over an area. Each AP has an antenna array. In previous studies, the DAS has been demonstrated great potential to improve capacity and power efficiency compared to a centralized antenna system (CAS) which has all the antennas located in one place. The existing research also has shown that the placement of the APs is essential for the performance of the DAS. However, most research on AP placement does not take into account realistic constraints. For instance, they assume that APs can be placed at any location in a region or the array radiation pattern of each AP is isotropic. This paper focuses on optimizing the AP placement for the DAS with massive MIMO (D-mMIMO) in order to reduce the transmit power. A square topology for the AP placement is applied, which is reasonable for deploying the D-mMIMO in urban areas while also offering theoretically interesting insights. We investigate the impact of the radiation pattern, signal coherence, and region size on the D-mMIMO's performance. Our results suggest that (i) among the three factors, the array radiation pattern of each AP is the most important one in determining the optimal AP placement for the D-mMIMO; (ii) the performance of the D-mMIMO is highly impacted by the placement and array radiation pattern of each AP, the D-mMIMO with unoptimized placement may perform even worse than the CAS with massive MIMO (C-mMIMO); (iii) with the consideration of patch antennas and the mutual coupling effect, the optimized D-mMIMO can potentially save more than 7dB transmit power compared to the C-mMIMO. Furthermore, our analytical results also provide an intuition for determining an adequate AP placement for the D-mMIMO in practice.

Channel estimation is essential in massive MIMO systems. Pilot Contamination (PC) however, causes a major bottleneck in the acquisition of this information. The exploitation of the Angle of Arrival (AoA) provides multiple techniques for channel estimation under PC. However, many AoA estimation techniques require information on the signal statistics which is not available in dynamic scenarios. In this paper we propose and analyse the Matrix Pencil Method (MPM) to decorrelate contaminated channels based on their estimated AoA. We evaluate this method both through simulations and experiments in a real-life testbed. Our assessment focuses on a system with a Uniform Linear Array (ULA). The performance of the MPM is validated through simulations1with varying number of antennas, SNR and AoA difference. The results show that our approach effectively decorrelates the channels starting from 20 antennas and an SNR of 15 dB, which outperforms the theoretical expectation. This allows us to enhance the channel estimation quality under PC to the level of no PC. Real-life measurements confirm the simulated results. Our MPM implementation can achieve a target AoA estimation accuracy both with and without PC. We anticipate that the method can be extended for a Uniform Rectangular Array (URA).1We would like to thank NVIDIA for providing the GPU that was used to greatly accelerate our simulations.

Low-power wide-area technologies have demonstrated their usefulness in a wide variety of Internet of Things applications. New applications are emerging, requiring a mesh or point-to-point (P2P) topology, in contrast to the conventional star-of-stars topology. In this article, we evaluate the coverage and model the path loss (PL) of these links, based on experimental campaigns in three environments: 1) urban; 2) forest; and 3) coastal. More obstructions, scattering, and diffraction are encountered because the terminals are typically at a low height. This results in a higher PL compared to the star-of-stars topology. Consequently, more packets drop below the receiver sensitivity. Realistic PL parameters are estimated by also taking the censored data into account. The packet error ratio is determined based on the estimated PL parameters to evaluate the performance of P2P links. Even in these adverse environments, 80% of the transmitted packets are successfully received at a distance of approximately 200 m. Moreover, a range of over 4 km is observed in the line-of-sight scenario. Despite the unfavorable radio propagation in the urban scenario and the densely forested terrain, a maximum range of 1 km is achieved.

Both physical and MAC-layer parameters impact the autonomy of IoT devices. We present an open-source cross-layer assessment framework for Low Power Wide Area networks (LPWANs) in this paper. It extends the state-of-the-art with energy models, downlink messages, and adaptive datarate features. Hence, hypotheses and transmission schemes can be tested and evaluated. As a representative case, the LoRaWAN protocol is assessed. The findings demonstrate that a cross-layer is imperative to effectively realize LPWANs in terms of energy efficiency and throughput. For instance, up to a factor of three reduction in energy consumption can be achieved by transmitting longer packet on quasi-static channels. Yet, under adverse dynamic conditions, an energy penalty will occur.