arXiv subjects

Andreas Burg

Publications and source records attributed to Andreas Burg.

At least 37 records · Page 2Linked to original sources

arXiv2023

Band-of-Interest-based Channel Impulse Response Fusion for Breathing Rate Estimation with UWB

The channel impulse response (CIR) obtained from the channel estimation step of various wireless systems is a widely used source of information in wireless sensing. Breathing rate is one of the important vital signs that can be retrieved from the CIR. Recently, there have been various works that extract the breathing rate from one carefully selected CIR delay bin that contains the breathing information. However, it has also been shown that the accuracy of this estimation is very sensitive to the measurement scenario, e.g., if there is any obstacle between the transceivers and the target, the position of the target, and the orientation of the target, since only one CIR delay bin does not contain a sufficient periodic component to retrieve the breathing rate. We focus on such scenarios and propose a CIR delay bin fusion method to merge several CIR bins to achieve a more accurate and reliable breathing rate estimate. We take measurements and showcase the advantages of the proposed method across scenarios.

eess.SP↗

arXiv2022

Single-anchor UWB Localization using Channel Impulse Response Distributions

Ultra-wideband (UWB) devices are widely used in indoor localization scenarios. Single-anchor UWB localization shows advantages because of its simple system setup compared to conventional two-way ranging (TWR) and trilateration localization methods. In this work, we focus on single-anchor UWB localization methods that learn statistical features of the channel impulse response (CIR) in different location areas using a Gaussian mixture model (GMM). We show that by learning the joint distributions of the amplitudes of different delay components, we achieve a more accurate location estimate compared to considering each delay bin independently. Moreover, we develop a similarity metric between sets of CIRs. With this set-based similarity metric, we can further improve the estimation performance, compared to treating each snapshot separately. We showcase the advantages of the proposed methods in multiple application scenarios.

eess.SP↗

arXiv2022

A Sequence Repetition Node-Based Successive Cancellation List Decoder for 5G Polar Codes: Algorithm and Implementation

Due to the low-latency and high-reliability requirements of 5G, low-complexity node-based successive cancellation list (SCL) decoding has received considerable attention for use in 5G communications systems. By identifying special constituent codes in the decoding tree and immediately decoding these, node-based SCL decoding provides a significant reduction in decoding latency compared to conventional SCL decoding. However, while there exists many types of nodes, the current node-based SCL decoders are limited by the lack of a more generalized node that can efficiently decode a larger number of different constituent codes to further reduce the decoding time. In this paper, we extend a recent generalized node, the sequence repetition (SR) node to SCL decoding and we describe the first implementation of an SR-List decoder. By merging certain SR-List decoding operations and applying various optimizations for 5G New Radio (NR) polar codes, our optimized SR-List decoding algorithm increases the throughput by almost ${2\times}$ compared to a similar state-of-the-art node-based SCL decoder. We also present our hardware implementation of the optimized SR-List decoding algorithm which supports all 5G NR polar codes. Synthesis results show that our SR-List decoder can achieve a $2.94 \, \mathrm{Gbps}$ throughput and $6.70\, \mathrm{Gbps} / \mathrm{mm}^2$ area efficiency for ${L=8}$.

cs.IT↗

arXiv2022

Increasing Cellular Network Energy Efficiency for Railway Corridors

Modern trains act as Faraday cages making it challenging to provide high cellular data capacities to passengers. A solution is the deployment of linear cells along railway tracks, forming a cellular corridor. To provide a sufficiently high data capacity, many cell sites need to be installed at regular distances. However, such cellular corridors with high power sites in short distance intervals are not sustainable due to the infrastructure power consumption. To render railway connectivity more sustainable, we propose to deploy fewer high-power radio units with intermediate low-power support repeater nodes. We show that these repeaters consume only 5 % of the energy of a regular cell site and help to maintain the same data capacity in the trains. In a further step, we introduce a sleep mode for the repeater nodes that enables autonomous solar powering and even eases installation because no cables to the relays are needed.

cs.NI↗

arXiv2022

OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting

Many applications require accurate indoor localization. Fingerprint-based localization methods propose a solution to this problem, but rely on a radio map that is effort-intensive to acquire. We automate the radio map acquisition phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we open-source a radio map acquired with our automated tool for a 3GPP Long-Term Evolution (LTE) wireless link. To the best of our knowledge, this is the first publicly available radio map containing channel state information (CSI). Finally, we describe first localization experiments on this radio map using a convolutional neural network to regress for location coordinates.

eess.SP↗

arXiv2021

Adding Indoor Capacity Without Fiber Backhaul: A mmWave Bridge Prototype

Today, a large portion of the mobile data traffic is consumed behind the shielding walls of buildings or in the Faraday cage of trains. This renders cellular network coverage from outdoor cell sites difficult. Indoor small cells and distributed antennas along train tracks are often considered as a solution, but the cost and the need for optical fiber backhaul are often prohibitive. To alleviate this issue, we describe an out-of-band repeater that converts a sub-6 GHz cell signal from a small cell installed at a cell tower to a mmWave frequency for the fronthaul to buildings or distributed antenna sites, where the signal is downconverted to the original frequency and emitted for example inside a building. This concept does not require fiber deployment, provides backward compatibility to equipment already in use, and additional indoor capacity is gained while outdoor networks are offloaded. The architecture and hardware prototype implementation are described, and measurements are reported to demonstrate the functionality and compatibility with commercial infrastructure and mobile terminals.

cs.NI↗

arXiv2021

A mmWave Bridge Concept to Solve the Cellular Outdoor-to-Indoor Challenge

Wireless indoor coverage and data capacity are important aspects of cellular networks. With the ever-increasing data traffic, demand for more data capacity indoors is also growing. The lower frequencies of the legacy frequency bands of macro outdoor cells manage to provide coverage inside buildings, however, new frequencies foreseen for the 5th generation (5G) of mobile communications in the millimeter wave (mmWave) spectrum penetrate very poorly into buildings. Therefore, a massive densification of the network would require to deploy a large number of indoor small cells, which would lead to high deployment costs to install the necessary wired/optical backhaul. Hence, other methods are needed that allow an increase of the data capacity indoors, bearing a lower cost than a fiber deployment. We propose a cost-efficient out-of-band repeater architecture that provides more data capacity indoors than an outdoor macro/micro network can provide to indoor, without adversely affecting a legacy network, and which readily works with the established cellular infrastructure as well as standard handsets/smartphones. This proposal is compared to conventional in- and out-of-band repeaters and relay nodes in order to highlight the advantages of our solution. While the data capacity for a single link is similar to that of repeaters and relays, a macro cell can be effectively offloaded. Cell capacities corresponding to at least 3--4 times that of a repeater or relay solution can be provided, depending on the number of parallel installed links and the bandwidth in the mmWave spectrum.

cs.NI↗

arXiv2021

3.5 GHz Coverage Assessment with a 5G Testbed

Today, cellular networks have saturated frequencies below 3\,GHz. Because of increasing capacity requirements, 5th generation (5G) mobile networks target the 3.5\,GHz band (3.4 to 3.8\,GHz). Despite its expected wide usage, there is little empirical path loss data and mobile radio network planning experience for the 3.5\,GHz band available. This paper presents the results of rural, suburban, and urban measurement campaigns using a pre-standard 5G prototype testbed operating at 3.5\,GHz, with outdoor as well as outdoor-to-indoor scenarios. Based on the measurement results, path loss models are evaluated, which are essential for network planning.

cs.NI↗

arXiv2021

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.

eess.SP↗

arXiv2020

A Maximum-Likelihood-based Multi-User LoRa Receiver Implemented in GNU Radio

LoRa is a popular low-power wide-area network (LPWAN) technology that uses spread-spectrum to achieve long-range connectivity and resilience to noise and interference. For energy efficiency reasons, LoRa adopts a pure ALOHA access scheme, which leads to reduced network throughput due to packet collisions at the gateways. To alleviate this issue, in this paper we analyze and implement a LoRa receiver that is able to decode LoRa packets from two interfering users. Our main contribution is a two-user detector derived in a maximum-likelihood fashion using a detailed interference model. As the complexity of the maximum-likelihood sequence estimation is prohibitive, a complexity-reduction technique is introduced to enable a practical implementation of the proposed two-user detector. This detector has been implemented along with an interference-robust synchronization algorithm on the GNU Radio Software-Defined-Radio (SDR) platform. The SDR implementation shows the effectiveness of the proposed method and also allows its experimental evaluation. Measurements indicate that our detector inherently leverages the time offset between the two colliding users to separate and demodulate their contributions.

eess.SP↗

arXiv2020

On the Advantage of Coherent LoRa Detection in the Presence of Interference

It has been shown that the coherent detection of LoRa signals only provides marginal gains of around 0.7 dB on the additive white Gaussian noise (AWGN) channel. However, ALOHA-based massive Internet of Things systems, including LoRa, often operate in the interference-limited regime. Therefore, in this work, we examine the performance of the LoRa modulation with coherent detection in the presence of interference from another LoRa user with the same spreading factor. We derive rigorous symbol- and frame error rate expressions as well as bounds and approximations for evaluating the error rates. The error rates predicted by these approximations are compared against error rates found by Monte Carlo simulations and shown to be very accurate. We also compare the performance of LoRa with coherent and non-coherent receivers and we show that the coherent detection of LoRa is significantly more beneficial in interference scenarios than in the presence of only AWGN. For example, we show that coherent detection leads to a 2.5 dB gain over the standard non-coherent detection for a signal-to-interference ratio (SIR) of 3 dB and up to a 10 dB gain for an SIR of 0 dB.

eess.SP↗

arXiv2020

Identification of Non-Linear RF Systems Using Backpropagation

In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate the effectiveness of this approach through the example of digital self-interference cancellation in full-duplex communications where an IQ imbalance model and a non-linear PA model are cascaded in series. For a self-interference cancellation performance of approximately 44.5 dB, the number of model parameters can be reduced by 74% and the number of operations per sample can be reduced by 79% compared to an expanded linear-in-parameters polynomial model.

eess.SP↗

arXiv2020

Hardware Implementation of Neural Self-Interference Cancellation

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1\times$ and $2\times$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.

eess.SP↗

arXiv2020

Lupulus: A Flexible Hardware Accelerator for Neural Networks

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, the high rate of innovation in machine learning makes it important that hardware implementations provide a high level of programmability to support current and future requirements of neural networks. In this work, we present a flexible hardware accelerator for neural networks, called Lupulus, supporting various methods for scheduling and mapping of operations onto the accelerator. Lupulus was implemented in a 28nm FD-SOI technology and demonstrates a peak performance of 380 GOPS/GHz with latencies of 21.4ms and 183.6ms for the convolutional layers of AlexNet and VGG-16, respectively.

eess.SP↗

arXiv2020

An Open-Source LoRa Physical Layer Prototype on GNU Radio

LoRa is the proprietary physical layer (PHY) of LoRaWAN, which is a popular Internet-of-Things (IoT) protocol enabling low-power devices to communicate over long ranges. A number of reverse engineering attempts have been published in the last few years that helped to reveal many of the LoRa PHY details. In this work, we describe our standard compatible LoRa PHY software-defined radio (SDR) prototype based on GNU Radio. We show how this SDR prototype can be used to develop and evaluate receiver algorithms for LoRa. As an example, we describe the sampling time offset and the carrier frequency offset estimation and compensation blocks. We experimentally evaluate the error rate of LoRa, both for the uncoded and the coded cases, to illustrate that our publicly available open-source implementation is a solid basis for further research.

eess.SP↗

arXiv2019

Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios

In-band full-duplex systems allow for more efficient use of temporal and spectral resources by transmitting and receiving information at the same time and on the same frequency. However, this creates a strong self-interference signal at the receiver, making the use of self-interference cancellation critical. Recently, neural networks have been used to perform digital self-interference with lower computational complexity compared to a traditional polynomial model. In this paper, we examine the use of advanced neural networks, such as recurrent and complex-valued neural networks, and we perform an in-depth network architecture exploration. Our neural network architecture exploration reveals that complex-valued neural networks can significantly reduce both the number of floating-point operations and parameters compared to a polynomial model, whereas the real-valued networks only reduce the number of floating-point operations. For example, at a digital self-interference cancellation of 44.51 dB, a complex-valued neural network requires 33.7 % fewer floating-point operations and 26.9 % fewer parameters compared to the polynomial model.

eess.SP↗

arXiv2019

On the Error Rate of the LoRa Modulation with Interference

LoRa is a chirp spread-spectrum modulation developed for the Internet of Things. In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model, which assumes perfect alignment of the signal of interest and the interference, to the more realistic case where the interfering user is neither chip- nor phase-aligned with the signal of interest and we derive an expression for the error rate. We show that the existing aligned interference model overestimates the effect of interference on the error rate. Moreover, we prove two symmetries in the interfering signal and we derive low-complexity approximate formulas that can significantly reduce the complexity of computing the symbol and frame error rates compared to the complete expression. Finally, we provide numerical simulations to corroborate the theoretical analysis and to verify the accuracy of our proposed approximations.

eess.SP↗

arXiv2019

Coded LoRa Frame Error Rate Analysis

In this work, we study the coded frame error rate (FER) of LoRa under additive white Gaussian noise (AWGN) and under carrier frequency offset (CFO). To this end, we use existing approximations for the bit error rate (BER) of the LoRa modulation under AWGN and we present a FER analysis that includes the channel coding, interleaving, and Gray mapping of the LoRa physical layer. We also derive the LoRa BER under carrier frequency offset and we present a corresponding FER analysis. We compare the derived frame error rate expressions to Monte Carlo simulations to verify their accuracy.

eess.SP↗