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Wireless power transfer (WPT) has garnered increasing attention due to its potential to eliminate device-side batteries. With the advent of (distributed) multiple-input multiple-output (MIMO), radio frequency (RF) WPT has become feasible over extended distances. This study focuses on optimizing the energy delivery to Energy Receivers (ERs) while minimizing system total transmit power. Rather than continuous power delivery, we optimize the precoding weights within specified time slots to meet the energy requirements of the ERs. Both unsynchronized (non-coherent) and synchronized (coherent) systems are evaluated. Our analysis indicates that augmenting the number of antennas and transitioning from an unsynchronized to asynchronized full phase-coherent system substantially enhances system performance. This optimization ensures precise energy delivery, reducing overshoots and overall energy consumption. Experimental validation was conducted using a testbed with84 antennas, validating the trends observed in our numerical simulations., Comment: 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

User mobility in extended reality (XR) can have a major impact on millimeter-wave (mmWave) links and may require dedicated mitigation strategies to ensure reliable connections and avoid outage. The available prior art has predominantly focused on XR applications with constrained user mobility and limited impact on mmWave channels. We have performed dedicated experiments to extend the characterisation of relevant future XR use cases featuring a high degree of user mobility. To this end, we have carried out a tailor-made measurement campaign and conducted a characterisation of the collected tracking data, including the approximation of the data using statistical distributions. Moreover, we have provided an interpretation of the possible impact of the recorded mobility on mmWave technology. The dataset is made publicly accessible to provide a testing ground for wireless system design and to enable further XR mobility modelling., Comment: In the process of being published in the IEEE Communications Magazine, special issue FT2304 / eXtended Reality

The Internet of Things (IoT) can support the evolution towards a digital and green future. However, the introduction of the technology clearly has in itself a direct adverse ecological impact. This paper assesses this impact at both the IoT-node and at the network side. For the nodes, we show that the electronics production of devices comes with a carbon footprint that can be much higher than during operation phase. We highlight that the inclusion of IoT support in existing cellular networks comes with a significant ecological penalty, raising overall energy consumption by more than 15%. These results call for novel design approaches for the nodes and for early consideration of the support for IoT in future networks. Raising the 'Vehicle or bandit?' question on the nature of IoT in the broader sense of sustainability, we illustrate the need for multidisciplinary cooperation to steer applications in desirable directions.

Indoor positioning applications are craving for ever higher precision and accuracy across the entire coverage zone. Optimal anchor placement and the deployment of multiple distributed anchor nodes could have a major impact in this regard. This paper examines the influences of these two difficult to approach hypotheses by means of a straightforward ultrasonic 3D indoor positioning system deployed in a real-life scenario via a geometric based simulation framework. To obtain an optimal anchor placement, a particle swarm optimization (PSO) algorithm is introduced and consequently performed for setups ranging from 4 to 10 anchors. In this way, besides the optimal anchor placement layout, the influence of deploying several distributed anchor nodes is investigated. In order to theoretically compare the optimization progress, a system model and Cram\'er-Rao lower bound (CRLB) are established and the results are quantified based on the simulation data. With limited anchors, the placement is crucial to obtain a high precision high reliability (HPHR) indoor positioning system (IPS), while the addition of anchors, to a lesser extent, gives a supplementary improvement.

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45 degrees from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD's azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel's spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.

Internet of Things (IoT) technology is named as a key ingredient in the evolution towards digitization of many applications and services. A deployment based on battery-powered remote Internet of Things (IoT) devices enables easy installation and operation, yet the autonomy of these devices poses a crucial challenge. A too short lifespan is undesirable from a functional, economical, and ecological point of view. This paper presents a unmanned aerial vehicle (UAV)-based approach to recharge remote Internet of Things (IoT) nodes. An in-depth study of the charging efficiency and optimization of key parameters, and measurements-based verification, is reported on. An actual corresponding design and implementation of the full UAV-based charging system and its proof-of-concept validation are presented. Finally, the sustainability of the proposed solution is discussed. The results presented in this paper hence confirm that the proposed UAV-based approach and design are functionally successful and efficient charging can be achieved, provided the constraints and challenges coming with the approach are adequately dealt with. Moreover, it comes with an overall reduction in ecological footprint for IoT applications relying on battery-powered nodes in need of medium energy and/or considerable lifetime expectation (5 years or more).

Keeping the batteries on the shelf: this is the holy grail for low-cost Internet of Things (IoT) nodes. In this paper we study the potential of radio frequency (RF)-based wireless power transfer implementing coherent beamforming with many antennas to realize this ambitious target. We optimize the deployment of the antennas to charge electronic shelf labels (ESLs), considering actual regulatory constraints. The results confirm the feasibility to create power spots that are sufficient to keep the high density of battery-less devices operational.

Immersing a user in life-like extended reality (XR) scenery using a head-mounted display (HMD) with a constrained form factor and hardware complexity requires remote rendering on a nearby edge server or computer. Millimeter-wave (mmWave) communication technology can provide sufficient data rate for wireless XR content transmission. However, mmWave channels exhibit severe sparsity in the angular domain. This means that distributed antenna arrays are required to cover a larger angular area and to combat outage during HMD rotation. At the same time, one would prefer fewer antenna elements/arrays for a lower complexity system. Therefore, it is important to evaluate the trade-off between the number of antenna arrays and the achievable performance to find a proper practical solution. This work presents indoor 28 GHz mmWave channel measurement data, collected during HMD mobility, and studies the dominant eigenmode (DE) gain. DE gain is a significant factor in understanding system performance since mmWave channel sparsity and eigenmode imbalance often results in provisioning the majority of the available power to the DE. Moreover, it provides the upper performance bounds for widely-adopted analog beamformers. We propose 3 performance metrics - gain trade-off, gain volatility, and minimum service trade-off - for evaluating the performance of a multi-array HMD and apply the metrics to indoor 28 GHz channel measurement data. Evaluation results indicate, that 3 arrays provide stable temporal channel gain. Adding a 4th array further increases channel capacity, while any additional arrays do not significantly increase physical layer performance.

Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distortion can coherently combine at user locations, limiting performance. We propose a graph neural network (GNN) to learn a mapping between channel and precoding matrices, which maximizes the sum rate affected by non-linear distortion, using a high-order polynomial PA model. In the distortion-limited regime, this GNN-based precoder outperforms zero forcing (ZF), ZF plus digital pre-distortion (DPD) and the distortion-aware beamforming (DAB) precoder from the state-of-the-art. At an input back-off of -3 dB the proposed precoder compared to ZF increases the sum rate by 8.60 and 8.84 bits/channel use for two and four users respectively. Radiation patterns show that these gains are achieved by transmitting the non-linear distortion in non-user directions. In the four user-case, for a fixed sum rate, the total consumed power (PA and processing) of the GNN precoder is 3.24 and 1.44 times lower compared to ZF and ZF plus DPD respectively. A complexity analysis shows six orders of magnitude reduction compared to DAB precoding. This opens perspectives to operate PAs closer to saturation, which drastically increases their energy efficiency.

The emergence of sixth-generation (6G) networks has spurred the development of novel testbeds, including sub-THz networks, cell-free systems, and 6G simulators. To maximize the benefits of these systems, it is crucial to make the generated data publicly available and easily reusable by others. Although data sharing has become a common practice, a lack of standardization hinders data accessibility and interoperability. In this study, we propose the Dataset Storage Standard (DSS) to address these challenges by facilitating data exchange and enabling convenient processing script creation in a testbed-agnostic manner. DSS supports both experimental and simulated data, allowing researchers to employ the same processing scripts and tools across different datasets. Unlike existing standardization efforts such as SigMF and NI RF Data Recording API, DSS provides a broader scope by accommodating a common definition file for testbeds and is not limited to RF data storage. The dataset format utilizes a hierarchical structure, with a tensor representation for specific experiment scenarios. In summary, DSS offers a comprehensive and flexible framework for enhancing the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) in 6G testbeds, promoting open and efficient data sharing in the research community.

Wireless time synchronization is one of the most important services in a Wireless Sensor Network (WSN). Inertial Measurement Units (IMUs) are often used in these WSNs in healthcare-related treatments. We present a low-power, wirelessly synchronized multi-IMU platform. The proposed approach synchronously captures packets from different IMUs and transmits the data over Bluetooth Low Energy (BLE) to a central Data Capturing Unit (DCU). The contribution of this work is, rather than focussing on the highest possible accuracy, to provide a low-power accurate enough solution for use in a multi-IMU WSN. We examine key factors affecting synchronization accuracy and elaborate on the implementation challenges. An accuracy of sub 1 us can be achieved with the approach using 74.8 J/h of energy, while a power-optimized implementation is presented with an accuracy of 200 us and an energy consumption of only 198 mJ/h. This approach suits the required accuracy and low-power requirements for a multi-IMU system.

The proof of the pudding is in the eating - that is why 6G testbeds are essential in the progress towards the next generation of wireless networks. Theoretical research towards 6G wireless networks is proposing advanced technologies to serve new applications and drastically improve the energy performance of the network. Testbeds are indispensable to validate these new technologies under more realistic conditions. This paper clarifies the requirements for 6G radio testbeds, reveals trends, and introduces approaches towards their development.

We present an indoor acoustic simulation framework that supports both ultrasonic and audible signaling. The framework opens the opportunity for fast indoor acoustic data generation and positioning development. The improved Pyroomacoustics-based physical model includes both an image-source model (ISM) and ray tracing method to simulate acoustic signaling in geometric spaces that extend typical shoe-box rooms. Moreover, it offers the convenience to facilitate multiple speakers and microphones with different directivity patterns. In addition to temperature and air absorption, the room reverberation is taken into account characterized by the RT60 value or the combination of building materials. Additional noise sources can be added by means of post processing and/or extra speakers. Indoor positioning methods assessed in simulation are compared with real measurements in a testbed, called 'Techtile'. This analysis confirms that the simulation results are close to the measurements and form a realistic representation of the reality. The simulation framework is constructed in a modular way, and parts can be replaced or modified to support different application domains. The code is made available open source.

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., Comment: This paper is presented at IEEE WCNC 2023. G. Callebaut, F. Rottenberg, L. Van der Perre, and E. G. Larsson, "Grant-Free random access of IoT devices in massive MIMO with partial CSI," in 2023 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2023), Glasgow, United Kingdom (Great Britain), Mar. 2023

In this paper, we investigate how Joint Communication And Sensing (JCAS) can be used to improve the Inertial Measurement Unit (IMU)- based tracking accuracy of eXtended Reality (XR) Head-Mounted Displays (HMDs). Such tracking is used when optical and InfraRed (IR) tracking is lost, and its lack of accuracy can lead to disruption of the user experience. In particular, we analyze the impact of using doppler-based speed estimation to aid the accelerometer-based position estimation, and Angle of Arrival (AoA) estimation to aid the gyroscope-based orientation estimation. Although less accurate than IMUs for short times in fact, the JCAS based methods require one fewer integration step, making the tracking more sustainable over time. Based on the proposed model, we conclude that at least in the case of the position estimate, introducing JCAS can make long lasting optical/IR tracking losses more sustainable., Comment: 6 pages, 5 figures

One of the use cases for 5G systems and beyond is ultra-reliability low-latency communication (URLLC). An enabling technology for URLLC is massive multiple-input multiple-output (MIMO), which can increase reliability due to improved user separation, array gain and the channel hardening effect. Measurements have been performed in an operating factory environment at 3.7 GHz with a co-located massive MIMO array and a unique randomly distributed array. Channel hardening can appear when the number of antennas is increased such that the variations of channel gain (small-scale fading) is decreased and it is here quantified. The cumulative distribution function (CDF) of the channel gains then becomes steeper and its tail is reduced. This CDF is modeled and the required fading margins are quantified. By deploying a distributed array, the large-scale power variations can also be reduced, further improving reliability. The large array in this rich scattering environment, creates a more reliable channel as it approaches an independent identically distributed (i.i.d.) complex Gaussian channel, indicating that one can rethink the system design in terms of e.g. channel coding and re-transmission strategies, in order to reduce latency. To conclude, massive MIMO is a highly interesting technology for reliable connectivity in reflective and heavily shadowed industrial environments., Comment: Submitted to JSAC

Multiple LPWANs have been rolled out to support the variety of IoT applications that are crucial to the ongoing digital transformation. 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.

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., Comment: arXiv admin note: text overlap with arXiv:2110.07891

The proposed infrastructure, named Techtile, provides a unique R&D facility as features dispersed electronics enables transmission and capturing of a multitude of signals in 3D. Specific available equipment that enhances the design process from smooth prototyping to a commercial product is discussed. The acoustic parameters of the room, particularly the reverberation and ambient noise, are measured to take these into account for future innovative acoustic indoor positioning and sensing systems. This can have a positive influence on the accuracy and precision. The wooden construction represents an acoustically challenging room for audible sound with a maximum measured RT60 value of 1.17s at 5kHz, which is comparable to the reverberation properties of a lecture or small concert hall. For ultrasound it is rather challenging due to the present ambient noise sources. In general, the Techtile room can be compared with a home or quiet office environment, in terms of sound pressure levels (SPLs). In addition to the acoustic properties, possible research and development options are discussed in combination with the associated challenges. Many of the designs described are available through open source., Comment: 6 pages

Cell-Free networking is one of the prime candidates for 6G networks. Despite being capable of providing the 6G needs, practical limitations and considerations are often neglected in current research. In this work, we introduce the concept of federations to dynamically scale and select the best set of resources, e.g., antennas, computing and data resources, to serve a given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing, further increasing the complexity of such systems. Therefore, each federation is self-managing and is distributed over the area in a cell-free manner. Next to the dynamic federations, new accompanying terminology is proposed to design cell-free systems taking into account practical limitations such as time synchronization and distributed processing. We conclude with an illustration with four federations, serving distinct applications, and introduce two new testbeds to study these architectures and concepts.

New concepts for next-generation wireless systems are being developed. It is expected that these 6G and beyond systems will incorporate more than only communication, but also sensing, positioning, (deep) edge computing, and other services. The discussed measurement facility and approach, named Techtile, is an open, both in design and operation, and unique testbed to evaluate these newly envisioned systems. Techtile is a multi-functional and versatile testbed, providing fine-grained distributed resources for new communication, positioning and sensing technologies. The facility enables experimental research on hyper-connected interactive environments and validation of new algorithms and topologies. The backbone connects 140~resource units equipped with edge computing devices, software-defined radios, sensors, and LED sources. By doing so, different network topologies and local-versus-central computing can be assessed. The introduced diversity of i) the technologies (e.g., RF, acoustics and light), ii) the distributed resources and iii) the interconnectivity allows exploring more degrees and new types of diversity, which can be investigated in this testbed., Comment: arXiv admin note: substantial text overlap with arXiv:2105.06740

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 -3 dB.

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 taken 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.

RF Wireless Power Transfer (WPT) emerges as a technology for charging autonomous devices, enabling simultaneous power and information transfer. However, with increasing distance, single-input, single-channel rectenna systems are not able to meet the power requirements of large scale IoT applications. In this paper, we tackle this problem on two levels. First, we minimize the energy consumption at the energy-constrained device on three levels. Second, we evolve to a dual-band solution increasing RF WPT. One frequency band is used to provide a base charge to many nodes in a shared transmission. Beam steering, on the other hand, allows for more power hungry operations while introducing as minimal interference as possible. We showcase this method for a hybrid RF-acoustic positioning system. Practical measurements conducted in a multi-antenna indoor testbed (Techtile) show the additional power gain and positioning rate.

Massive Multiple Input Multiple Output (MIMO) offers superior capacity for future networks. In the quest for energy efficient implementation of these large array-based trans-mission 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.

Distributed massive MIMO (D-mMIMO) has been considered for future networks as it holds the potential to offer superior capacity while enabling energy savings in the network. A D-mMIMO system has multiple arrays. Optimizing the locations of the arrays is essential for the energy efficiency of the system. In existing works, array placement has been optimized mostly based on common channel models, which rely on a given statistical distribution and Euclidean distance between user locations and arrays. These models are justified if applied to sufficiently large cells, where the statistical description of the channel is expected to fit its empirical condition. However, with the advent of small cells, this is no longer the case. The channel propagation condition becomes highly environment-specific. This paper investigates array placement optimization with different ways of modeling the propagation conditions taking the environment information (e.g., buildings) into account. We capture the environment information via a graph. Two shortest path-based propagation models are introduced based on the graph. We validate the performance of the models with a ray-tracing simulator considering different signal coverage levels. The simulation results demonstrate that higher energy efficiency is achieved by using the array placements found via the proposed models compared to a Euclidean distance-based propagation model in small cells. The average power saving at 96% signal coverage, for example, reaches more than 5 dB.

Massive MIMO is a promising technology to enable a massive number of Internet of Things nodes to transmit short and sporadic data bursts at low power. In conventional cellular networks, devices use a grant-based random access scheme to initiate communications. This scheme relies on a limited set of orthogonal preambles, which simplify signal processing operations at network access points. However, it is not well suited for Internet of Things (IoT) devices due to: (i) the large protocol overhead, and (ii) the high probability of collision. In contrast to the grant-based scheme, a grant-free approach uses user-specific preambles and has a small overhead, at the expense of more complexity at access points. In this work, a grant-free method is proposed, applicable for both co-located and cell-free deployments. The method has a closed form solution, which results in a significantly low complexity with respect to the state-of-the art. The algorithm exploits the static nature of IoT devices through the use of prior channel state information. With a power budget of 1 mW, 64 antennas are sufficient to support 1000 nodes with 200 simultaneous access requests with a probability of false alarm and miss detection below 10-6 and 10-4, respectively.

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 \gls{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 \gls{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.

Due to the nonlinearity of power amplifiers (PAs), the transmit signal is distorted. Previous works have studied the spatial distribution of this distortion for a central massive MIMO array. In this work, we extend the analysis for distributed massive MIMO in a line-of-sight scenario. We show that the distortion is not always uniformly distributed in space. In the single-user case, it coherently combines at the user location. In the few users case, the signals will add up at the user locations plus several others locations. As the number of users increases, it becomes close to uniformly distributed. As a comparison with a central massive MIMO system, having the same total number of antennas, the distortion in distributed massive MIMO is considerably more uniformly distributed in space. Moreover, the potential coherent combining is contained in a zone rather than in generic directions, i.e., in a beamspot. Furthermore, a small-scale fading effect is observed at unintended locations due to the non-coherent combining of the signals. As a result, one can expect that going towards distributed systems allows working closer to saturation, increasing the PA operating efficiency and/or using low-cost PAs.

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 7 dB 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., Comment: IEEE VTC2021 fall Conference, 6 pages, 7 figures

The wireless communications landscape is anticipated to offer new service levels following the introduction of the millimeter-wave (mmWave) spectrum to consumer electronics. With their broad bandwidths and corresponding multi-Gbps data rates, these mmWaves are a perfect fit for data hungry applications, such as streaming video to extended reality devices. However, the latter are also bound by maximal latency constraints as low as 1 ms. Understanding where such minuscule time delays lurk requires a close-up study of individual layers in the network stack. Starting from the bottom up, the present work describes an endeavor at uncloaking the origins of physical layer (PHY) latency in mmWave Wi-Fi networks. It proposes a newly designed simulation framework and sheds light on how any conventional laboratory can be turned into a virtual experiment setting, speeding up computation. A case study based on the IEEE 802.11ad standard demonstrates the framework's ability to track packet latency at the PHY-level and identify individual bottlenecks. In particular, it evaluates the impact of the number of LDPC decoding iterations on latency in short transmission sequences., Comment: This work has been accepted to the 41st WIC Symposium On Information Theory and Signal Processing in the Benelux (SITB 2021)

Due to the increase of Internet-of-Things (IoT) devices, IoT networks are getting overcrowded. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections, while also lowering the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit-power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.

The Techtile measurement infrastructure is a multi-functional, versatile testbed for new communication and sensing technologies relying on fine-grained distributed resources. The facility enables experimental research on hyper-connected interactive environments and validation of new wireless connectivity, sensing and positioning solutions. It consists of a data acquisition and processing equipment backbone and a fabric of dispersed edge computing devices, Software-Defined Radios, sensors, and LED sources. These bring intelligence close to the applications and can also collectively function as a massive, distributed resource. Furthermore, the infrastructure allows exploring more degrees and new types of diversity, i.e., scaling up the number of elements, introducing `3D directional diversity' by deploying the distributed elements with different orientations, and `interface diversity' by exploiting multiple technologies and hybrid signals (RF, acoustic, and visible light)., Comment: has been accepted at the 41stWIC Symposium On Information Theory and Signal Processing in the Benelux (SITB 2021)

The broad range of requirements of Internet of Things applications has lead to the development of several dedicated communication technologies, each tailored to meet a specific feature set. A solution combining different wireless technologies in one device, can overcome the disadvantages of any individual technology. The design of such Multiple Radio Access Technology solutions based on the diverse characteristics of the technologies offers interesting opportunities. In this work we analyze the potential of combining LoRaWAN and NB-IoT in a Multi-RAT solution for IoT. To that end we evaluate key IoT node requirements in function of payload size and link quality: (1) energy efficiency, (2) coverage, (3) payload size, (4) latency performance, (5) Quality of Service, and (6) cost efficiency. Our theoretical assessment and experimental validation of these IoT features show the merits of a Multi-RAT solution. Notably, energy consumption in use cases with only sporadic large payload requirements, can be improved by a factor of at least 4 with respect to either single-mode technologies. Moreover, latency-critical messages can get delivered on time and coverage can be extended elegantly where needed., Comment: 7 pages, 6 figures

We present a hybrid signaling approach to position energy-neutral devices. Our method combines the best of two worlds: instant RF signals for both communication and energy transfer, and slower propagating acoustic waves for accurate distance measurements. We introduce advanced energy loading of an 'E-buffer' through beamformed RF signals. The scientific contribution of our approach is twofold, advancing both the positioning performance and the energy harvesting efficiency. On the one hand, we overcome current distance limitations in RF backscattering-based indoor localization. On the other hand, it enhances the energy harvesting by using the calculated position and beamforming a higher amount of directed RF energy into the mobile node. We provide a functional architecture for both sides of the system and present a proof-of-concept. Practical measurements in representative use cases show an update rate of 10 positions per hour within the regulatory constraints in the 868MHz band for distances up to 4.5 m. An energy and power model are drawn up to provide insights into the performance trade-offs., Comment: This work is accepted by IEEE Asilomar 2020. This work was partially funded by the FWO project on location-locked cryptographic solutions for energy constrained devices under grant agreement G0D3819N 5 pages, 6 figures, 1 Table

Low-Power Wide-Area Networks operating in the unlicensed bands are being deployed to connect a rapidly growing number of Internet-of-Things devices. While the unlicensed sub-GHz band offers favorable propagation for long-range connections, measurements show that the energy consumption of the nodes is still mostly dominated by the wireless transmission affecting their autonomy. We investigate the potential benefits of deploying massive MIMO technology to increase system reliability and at the same time support low-energy devices with good coverage at sub-GHz frequencies. The impact of different antenna configurations and propagation conditions is analyzed. Both actual average experienced array gain and channel hardening are examined. The assessment demonstrates the effect of channel hardening as well as the potential benefits of the experienced array gain. These measurements serve as a first assessment of the channel conditions of massive MIMO at sub-GHz frequencies and are, to the best of our knowledge, the first of its kind.

Massive MIMO is a leading technology to connect very large numbers of energy constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. We here propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency.

Reliability is becoming increasingly important for many applications envisioned for future wireless systems. A technology that could improve reliability in these systems is massive MIMO (Multiple-Input Multiple-Output). One reason for this is a phenomenon called channel hardening, which means that as the number of antennas in the system increases, the variations of channel gain decrease in both the time- and frequency domain. Our analysis of channel hardening is based on a joint comparison of theory, measurements and simulations. Data from measurement campaigns including both indoor and outdoor scenarios, as well as cylindrical and planar base station arrays, are analyzed. The simulation analysis includes a comparison with the COST 2100 channel model with its massive MIMO extension. The conclusion is that the COST 2100 model is well suited to represent real scenarios, and provides a reasonable match to actual measurements up to the uncertainty of antenna patterns and user interaction. Also, the channel hardening effect in practical massive MIMO channels is less pronounced than in complex independent and identically distributed (i.i.d.) Gaussian channels, which are often considered in theoretical work., Comment: Accepted to IEEE Open Journal of the Communications Society

Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 {\mu}W for a single measurement is achieved. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning., Comment: 19 pages, 18 figures, 5 tables

We present a new type of wireless access infras-tructure consisting of a fabric of dispersed electronic circuitsand antennas that collectively function as a massive, distributed antenna array. We have chosen to name this new wireless infrastructure 'RadioWeaves' and anticipate they can be integrated into indoor and outdoor walls, furniture, and otherobjects, rendering them a natural part of the environment. Technologically, RadioWeaves will deploy distributed arrays to create both favorable propagation and antenna array interaction. The technology leverages on the ideas of large-scale intelligent surfaces and cell-free wireless access. Offering close to the service connectivity and computing, new grades in energy efficiency,reliability, and low latency can be reached. The new concept moreover can be scaled up easily to offer a very high capacity inspecific areas demanding so. In this paper we anticipate how two different demanding use cases can be served well by a dedicated RadioWeaves deployment: a crowd scenario and a highly reflective factory environment. A practical approach towards a RadioWeaves prototype, integrating dispersed electronics invisibly in a room environment, is introduced. We outline diverse R\&D challenges that need to be addressed to realize the great potential of the RadioWeaves technology., Comment: Presented at Asilomar 2019, publication in conference proceedings accepted

This paper focuses on Point-to-Point LoRa connections. We report on coverage measurements performed in three distinct environments, i.e., coastal, forest and urban. Our field experiments demonstrate coverage up to 1 km with antennas at only 1.5 m height in an urban scenario. In free Line-of-Sight (LoS) this coverage is extended to 4 km. Based on these results, we are developing a path loss model in future work. The developed hardware and software including measurements are open-source., Comment: This paper is accepted and published in the Conference Proceedings of 2019 European Conference on Networks and Communications (EuCNC)

Long range wireless connectivity opens the door for new IoT applications. Low energy consumption is essential to enable long autonomy of devices powered by batteries or even relying on harvested energy. This paper introduces technologies for long-distance interaction with energy-constraint embedded devices. It proposes sleepy strategies for power management for the platform and the transmission. Adequate signal processing on the remote modules is demonstrated to play a crucial role to sustain the autonomy of these systems. The presented open-source development platform invites the signal processing community to a smooth validation of new algorithms and applications.

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested., Comment: submitted to IEEE transactions on signal processing

Both physical and MAC-layer need to be jointly optimized to maximize the autonomy of IoT devices. Therefore, a cross-layer design is imperative to effectively realize Low Power Wide Area networks (LPWANs). In the present paper, a cross-layer assessment framework including power modeling is proposed. Through this simulation framework, the energy consumption of IoT devices, currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a cross-layer approach significantly improves energy efficiency and overall throughput. Two major contributions are made. First, an open-source LPWAN assessment framework has been conceived. It allows testing and evaluating hypotheses and schemes. Secondly, as a representative case, the LoRaWAN protocol is assessed. The findings indicate how a cross-layer approach can optimize LPWANs in terms of energy efficiency and throughput. For instance, it is shown that the use of larger payloads can reduce up to three times the energy consumption on quasi-static channels yet may bring an energy penalty under adverse dynamic conditions.

Massive MIMO is considered a key technology for 5G. Various studies analyze the impact of the number of antennas, relying on channel properties only and assuming uniform antenna gains in very large arrays. In this paper, we investigate the impact of mutual coupling and edge effects on the gain pattern variation in the array. Our analysis focuses on the comparison of patch antennas versus dipoles, representative for the antennas typically used in massive MIMO experiments today. Through simulations and measurements, we show that the finite patch array has a lower gain pattern variation compared with a dipole array. The impact of a large gain pattern variation on the massive MIMO system is that not all antennas contribute equally for all users, and the effective number of antennas seen for a single user is reduced. We show that the effect of this at system level is a decreased rate for all users for the zero-forcing MIMO detector, up to 20% for the patch array and 35% for the dipole array. The maximum ratio combining on the other hand, introduces user unfairness., Comment: 9 pages, IEEE Transactions on Antennas and Propagation

Wireless-controlled robots, cars and other critical applications are in need of technologies that offer high reliability and low latency. Massive MIMO, Multiple-Input Multiple-Output, is a key technology for the upcoming 5G systems and is one part of the solution to increase the reliability of wireless systems. More specifically, when increasing the number of base station antennas in a massive MIMO systems the channel variations decrease and the so-called channel hardening effect appears. This means that the variations of the channel gain in time and frequency decrease. In this paper, channel hardening in massive MIMO systems is assessed based on analysis of measurement data. For an indoor scenario, the channels are measured with a 128-port cylindrical array for nine single-antenna users. The analysis shows that in a real scenario a channel hardening of 3.2-4.6 dB, measured as a reduction of the standard deviation of the channel gain, can be expected depending on the amount of user interaction. Also, some practical implications and insights are presented., Comment: Accepted to SPAWC 2018

The use of base stations (BSs) and access points (APs) with a large number of antennas, called Massive MIMO (multiple-input multiple-output), is a key technology for increasing the capacity of 5G networks and beyond. While originally conceived for conventional sub-6GHz frequencies, Massive MIMO (mMIMO) is ideal also for frequency bands in the range 30-300 GHz, known as millimeter wave (mmWave). Despite conceptual similarities, the way in which mMIMO can be exploited in these bands is radically different, due to their specific propagation behaviors and hardware characteristics. This paper reviews these differences and their implications, while dispelling common misunderstandings. Building on this foundation, we suggest appropriate signal processing schemes and use cases to efficiently exploit mMIMO in both frequency bands., Comment: To appear in IEEE Wireless Communications, February 2019

Non-linearities in radio-frequency (RF) transceiver hardware, particularly in power amplifiers, cause distortion in-band and out-of-band. Contrary to claims made in recent literature, in a multiple-antenna system this distortion is correlated across the antennas in the array. A significant implication of this fact is that out-of-band emissions caused by non-linearities are beamformed, in some cases into the same direction as the useful signal., Comment: IEEE Wireless Communications Letters, 2018, to appear