An SDR-Based Monostatic Wi-Fi System with Analog Self-Interference Cancellation for Sensing

Wireless sensing offers an alternative to wearables for contactless monitoring of human activity and vital signs. However, most existing systems use bistatic setups, which suffer from phase imperfections due to unsynchronized clocks. Monostatic systems overcome this issue, but are hindered by strong self-interference (SI) that require effective cancellation. We present a monostatic Wi-Fi sensing system that uses an auxiliary transmit RF chain to achieve SI cancellation levels of 40 dB, comparable to existing solutions with custom cancellation hardware. We demonstrate that the cancellation filter weights, fine-tuned using least-mean squares, can be directly repurposed for target sensing. Moreover, we achieve stable SI cancellation over 30 minutes in an office environment without fine-tuning, enabling traditional vital sign monitoring using channel estimates derived from baseband samples without the adaptation of the cancellation affecting the sensing channel -- a significant limitation in prior work. Experimental results confirm the detection of small, slow-moving targets, representative for breathing chest movements, at distances up to 10 meters in non-line-of-sight conditions.

A High-Performance and Low-Complexity 5G LDPC Decoder: Algorithm and Implementation

5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR blocklengths and code rates, which is a significant challenge. In this paper, we present a high-performance and low-complexity LDPC decoder, tailor-made to fulfill the 5G requirements. First, to close the gap between belief propagation (BP) decoding and its approximations in hardware, we propose an extension of adjusted min-sum decoding, called generalized adjusted min-sum (GA-MS) decoding. This decoding algorithm flexibly truncates the incoming messages at the check node level and carefully approximates the non-linear functions of BP decoding to balance the error-rate and hardware complexity. Numerical results demonstrate that the proposed fixed-point GAMS has only a minor gap of 0.1 dB compared to floating-point BP under various scenarios of 5G standard specifications. Secondly, we present a fully reconfigurable 5G NR LDPC decoder implementation based on GA-MS decoding. Given that memory occupies a substantial portion of the decoder area, we adopt multiple data compression and approximation techniques to reduce 42.2% of the memory overhead. The corresponding 28nm FD-SOI ASIC decoder has a core area of 1.823 mm2 and operates at 895 MHz. It is compatible with all 5G NR LDPC codes and achieves a peak throughput of 24.42 Gbps and a maximum area efficiency of 13.40 Gbps/mm2 at 4 decoding iterations.

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.

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.

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.

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.

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.

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.

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.

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.