Acoustic feedback arises from the leakage of sound from the loudspeaker back to the microphone and is a common problem in hearing aids and public address systems. One popular solution to this problem is adaptive feedback cancellation (AFC) which utilizes an adaptive filter (AF) to estimate and subsequently subtract the feedback component from the microphone. Due to the closed-loop system the loudspeaker signal and the incoming signal exhibit a high correlation. However, model-based and more recently learning-based AF methods typically neglect this correlation in their derivation, leading to sub-optimal performance in closed-loop scenarios. In this paper, we propose Neural-AFC, a recurrent neural network (RNN) designed for AFC step-size control optimization, that addresses this problem by including not only the adaptive filter but also the acoustic feedback path and the system gain in the recurrence model. Our experiment results show that when trained within this closed-loop model, Neural-AFC improves the steady-state performance and (re)convergence rate compared to conventional and open-loop RNN-based AF in both hearing aid and public address system applications.

The hear-through functionality on hearing devices, which allows hearing equivalent to the open-ear while providing the possibility to modify the sound pressure at the eardrum in a desired manner, has drawn great attention from researchers in recent years. To this end, the output of the device is processed by means of an equalization filter, such that the transfer function between external sound sources and the eardrum is equivalent for the open-ear and the aided condition with the device in the ear. To achieve an ideal performance, the equalization filter design assumes the exact knowledge of all the relevant acoustic transfer functions. A particular challenge is the transfer function between the hearing device receiver and the eardrum, which is difficult to obtain in practice as it requires additional probe-tube measurements. In this work, we address this issue by proposing an individualized hear-through equalization filter design that leverages the measurement of the so-called secondary path to predict the sound pressure at the eardrum using a principle component analysis based estimator. Experimental results using real-ear measured transfer functions confirm that the proposed method achieves a good sound quality compared to the open-ear while outperforming filter designs that do not leverage the proposed estimator.

Automated vehicles will be able to drive autonomously in various environments. An essential part of that is to predict other vehicles' intents and to coordinate maneuvers jointly. Such cooperative maneuvers have the ability to make driving safer and traffic more efficient. However, among the various communication protocols proposed for maneuver coordination, no single one satisfies all requirements. This paper assesses failure risks and mitigation strategies for cooperative maneuvers, including an analysis of popular protocols regarding this aspect. Next, we evaluate one particular cooperation protocol, the complex vehicular interactions protocol (CVIP), concerning performance of mitigation mechanisms and their influence on maneuver success rates or times to reach consensus among maneuver participants. Via simulation, we show that CVIP is suitable for cooperative maneuvers in realistic scenarios and investigate the trade-offs individual mitigation mechanisms face. These results are well-suited as guidelines and benchmark for other researchers developing cooperative maneuver protocols.

Cooperative maneuvers will enable automated vehicles to optimize traffic flow and increase safety via vehicle-to-vehicle communication. Different approaches and protocols exist, but no study has investigated how to generate intelligent suggestions for cooperative maneuvers. We use machine learning to propose safe and suitable overtake maneuvers. To this end, we train a classifier for maneuver success as well as regression models on an extensive data set of randomized initial situations. In addition, we show that changing objective functions allows optimizing for different goals like smoothness or driven distance. Our evaluation shows that machine learning is well-suited to suggest cooperative maneuvers while also facing some trade-offs. This work may thus provide a benchmark for advanced studies on cooperative maneuver proposals.

Near-end listening enhancement (NELE) algorithms aim to pre-process speech prior to playback via loudspeakers so as to maintain high speech intelligibility even when listening conditions are not optimal, e.g., due to noise or reverberation. Often NELE algorithms are designed for scenarios considering either only the detrimental effect of noise or only reverberation, but not both disturbances. In many typical applications scenarios, however, both factors are present. In this paper, we evaluate a new combination of a noise-dependent and a reverberation-dependent algorithm implemented in a common framework. Specifically, we use instrumental measures as well as subjective ratings of listening effort for acoustic scenarios with different reverberation times and realistic signal-to-noise ratios. The results show that the noise-dependent algorithm also performs well in reverberation, and that the combination of both algorithms can yield slightly better performance than the individual algorithms alone. This benefit appears to depend strongly on the specific acoustic condition, indicating that further work is required to optimize the adaptive algorithm behavior.

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Cognitive radio (CR) is considered an enabler of wireless technologies in safety-critical applications such as connected mobility and industrial automation, due to its high reliability and resource efficiency. Here, accurate and fast estimation of the quality of the radio channel is crucial to CR's adaptive capability. In this paper, we propose a channel quality estimation architecture that devises a novel quality metric based solely on physical layer (PHY) measurements. The proposed metric utilizes a decision tree of PHY sub-metrics, without decoding the packets at higher layers. We validate the proposed architecture using a software defined radio (SDR) experimental setup, and investigate real-time capability and hardware implementation complexity. Our measurements show a high correlation between the proposed metric and the application layer quality of service (QoS). Our measurements also show that by using efficient software implementation, complexity is substantially reduced, which enables real-time functionality.

Cellular vehicle-to-everything (C-V2X) communication is a key enabler for future cooperative automated driving and safety-related applications. The requirements they demand in terms of Quality of Service (QoS) performance vary according to the use case. For instance, Day-1 applications such as Emergency Brake Light warning have less strict requirements than remote driving. In this paper, we seek to answer two questions: Are current LTE networks ready to support Day-1 applications at all times? And, can underperforming situations be reliably predicted based on GPS and network-related information? To address these questions, we first implement a system that collects positioning data and LTE key performance indicators (KPIs) with a higher time resolution than commercial off-the-shelf LTE modems, while simultaneously measuring the end-to-end (E2E) delay of an LTE network. We then use this system to assess the readiness of multiple mobile network operators (MNOs) and a live Mobile Edge Computing (MEC) deployment in an urban scenario. For evaluating whether an adaptable operation is possible in adverse circumstances, e.g., by performing hybrid networking or graceful degradation, we finally use Machine Learning to generate a client-based QoS predictor and forecast the achievable QoS levels.

Acoustic feedback occurs in hearing aids due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter typically converges to a biased optimal solution due to the closed-loop acoustical system of the hearing aid. Previously it has therefore been proposed to suppress the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers using a fixed null-steering beamformer and hence avoiding a biased adaption. While previous null-steering beamforming approaches aimed at perfect preservation of the incoming signal using its relative transfer function (RTF), in this article we propose to use a soft constraint that allows to trade off between incoming signal preservation and feedback suppression. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min-max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental evaluations were performed using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha. Results show that the proposed fixed null-steering beamformer using the RTF-based soft constraint provides a reduction of the acoustic feedback by 7-8 dB compared to the previously proposed RTF-based hard constraint while limiting the distortions of the incoming signal in the beamformer output.

Speech playback (e.g., TV, radio, public address) becomes harder to understand in the presence of noise and reverberation.\nNELE (Near End Listening Enhancement) algorithms can improve intelligibility by modifying the signal before it is played back. Substantial intelligibility improvements have been achieved in the lab for both natural and synthetic speech. However, evidence is still scarce on how these algorithms work under conditions of realistic noise and reverberation.\n\nWe present a realistic test platform, featuring two representative everyday scenarios in which speech playback may occur (in the presence of both noise and reverberation): a domestic space (living room) and a public space (cafeteria). The generated stimuli are evaluated by measuring keyword accuracy rates in a listening test with normal hearing subjects.\n\nWe use the new platform to compare three state-of-theart NELE algorithms, employing either noise-adaptive or nonadaptive\nstrategies, and with or without compensation for reverberation.

The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel HiddenMarkov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical (ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.

Coexistence of wireless systems in unlicensed bands is considered a severe performance bottleneck, given the heterogeneous and uncoordinated nature of the wireless technologies. A promising approach to address this issue is to apply cognitive radio (CR) techniques, which are capable of accurately predicting the quality-of-service (QoS). This enables highly reliable QoS management and performance guarantees for applications with strict requirements, such as industrial automation or connected driving. Furthermore, accurate QoS prediction is very important for the reliability of safety critical applications. To this end, we present a novel analytical model for predicting the probability distribution of the latency based on Markov Chains (MC) for transmission systems, which employ Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) as a medium access scheme. Further, we validate the high accuracy of the prediction model using ns-3 simulations of an IEEE 802.11n communication.

In hearing aids, acoustic feedback occurs due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter converges to an optimal solution that is typically biased due to the closed-loop acoustical system of the hearing aid. In order to avoid the adaptation to a biased optimal solution, in this paper we propose to use a fixed beamformer to cancel the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers. By steering a spatial null in the direction of the hearing aid loudspeaker, we show that theoretically perfect feedback cancellation can be achieved. While previous null-steering beamforming approaches did not control for distortions of the incoming signal, in this paper we propose to incorporate a constraint based on the relative transfer function (RTF) of the incoming signal, aiming to perfectly preserve this signal. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min-max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental results using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha show that the proposed fixed null-steering beamformer using the RTF-based constraint provides a reduction of the acoustic feedback and substantially increases the added stable gain while preserving the incoming signal. This can even be achieved for unknown acoustic feedback paths and incoming signal directions.

A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method (PEM) is a popular method for reducing this bias for adaptive feedback control (AFC) in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC (PEM-AFC) applications are still limited to single-microphone single-loudspeaker (SMSL) systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control (AFC2) method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function (RTF) which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.

Commonly adaptive filters are used to reduce the acoustic feedback in hearing aids. While theoretically allowing for perfect cancellation of the feedback signal, in practice the adaptive filter solution is typically biased due to the closed-loop hearing aid system. In contrast to conventional behind-the-ear hearing aids, in this paper we consider an earpiece with multiple integrated microphones. For such an earpiece it has previously been proposed to use a fixed beamformer to reduce the acoustic feedback in the microphones which has been designed to minimize a least-squares cost function. In this paper we propose to design the beamformer by minimizing a min-max cost function which directly maximizes the maximum stable gain of the earpiece. Furthermore, we propose a robust extension of the min-max cost function maximizing the worst-case maximum stable gain over a set of acoustic feedback paths. Experimental results using measured acoustic feedback paths show that the feedback cancellation performance of the fixed beamformer can be considerably improved by minimizing the proposed min-max optimization problem, while maintaining a high perceptual quality of the incoming signal.

The proportionate normalized least-mean-squares (PNLMS) algorithm is commonly used in acoustic echo cancellation (AEC) context. It provides faster initial convergence and tracking rates compared to the NLMS algorithm for the case of sparse echo impulse responses. The improved PNLMS algorithm (IPNLMS) has been proven to be more powerful than PNLMS by exploiting new rules for computing the weight of each step-size corresponding to each adaptive filter coefficient. However, the application of the PNLMS and the IPNLMS algorithms for adaptive feedback control (AFC) in hearing aids (HAs) is still limited due to high correlation between the loudspeaker and incoming signals. This paper proposes implementations of the PNLMS/IPNLMS algorithms for AFC using the prediction error method (PEM) for hearing aids. The proposed methods have been evaluated for both speech and music incoming signals. Simulation shows that the proposed methods have faster initial convergence and tracking than the PEM using the NLMS algorithm (PEM-NLMS).

In adaptive feedback cancellation an adaptive filter is used to model the acoustic feedback path between the hearing aid loudspeaker and the microphone. An important parameter for adaptive filters is the step-size, providing a trade-off between fast convergence and low steady-state misalignment. In order to achieve both fast convergence as well as low steady-state misalignment, it has been proposed to use an affine combination scheme of two filters operating with different step-sizes. In this paper we apply such an affine combination scheme to the acoustic feedback cancellation problem in hearing aids. We show that for speech signals a time-domain affine combination scheme yields a biased solution. To reduce this bias we propose to use a partitioned-block frequency-domain affine combination scheme. Experimental results using measured acoustic feedback paths show that in terms of misalignment and added stable gain the proposed adaptive feedback cancellation system outperforms a system that only uses a single adaptive filter with either of the fixed step-sizes used for the affine combination scheme.

The convergence speed and the computational complexity of adaptive feedback cancellation algorithms both depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback paths as the convolution of a time-invariant common part and time-varying variable parts. Instead of estimating all parameters of the common and variable parts by minimizing the misalignment using a least-squares cost function, in this paper we propose to formulate the parameter estimation problem as a min-max optimization problem aiming to maximize the maximum stable gain (MSG). We formulate the min-max optimization problem as a semidefinite program and use a constraint based on Lyapunov theory to guarantee stability of the estimated common pole-zero filter. Experimental results using measured acoustic feedback paths show that the proposed min-max optimization outperforms least-squares optimization in terms of the MSG. Furthermore, the results indicate that the proposed common part decomposition is able to increase the MSG and reduce the number of variable part parameters even for unknown feedback paths that were not included in the optimization. Simulation results using an adaptive feedback cancellation algorithm based on the prediction-error-method show that the convergence speed can be increased by using the proposed feedback path decomposition.

In adaptive feedback cancellation both the convergence speed and the computational complexity depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters, it has been proposed to model the acoustic feedback paths as the convolution of a time-invariant common pole-zero filter and time-varying all-zero filters, enabling to track fast changes. In this paper, a novel procedure to estimate the common pole-zero filter of acoustic feedback paths is presented. In contrast to previous approaches which minimize the so-called equation-error, we propose to approximate the desired output-error minimization by employing a weighted least-squares procedure motivated by the Steiglitz-McBride iteration. The estimation of the common pole-zero filter is formulated as a semidefinite programming problem, to which a constraint based on the Lyapunov theory is added in order to guarantee the stability of the estimated pole-zero filter. Experimental results using measured acoustic feedback paths from a two microphone behind-the-ear hearing aid show that the proposed optimization procedure using the Lyapunov constraint outperforms existing optimization procedures in terms of modelling accuracy and added stable gain.

The computational complexity and convergence speed of adaptive feedback cancellation algorithms depend on the number of adaptive parameters used to model the acoustic feedback path. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback path as the convolution of a (time-invariant) common part and a (time-varying) variable part. Typically the problem of estimating all the required coefficients has been formulated as a least-squares optimization problem. In contrast, in this paper we propose to formulate the estimation problem as a minmax optimization problem and show how this is associated with the maximum stable gain of a hearing aid. Experimental results using measured acoustic feedback paths from a two-microphone behind-the-ear hearing aid show that the proposed minmax optimization outperforms the least-squares optimization in terms of maximum stable gain. Furthermore, the robustness of proposed common part decomposition for different feedback paths is evaluated.

Speech intelligibility is an important factor for successful speech communication in today’s society. So-called near-end listening enhancement (NELE) algorithms aim at improving speech intelligibility in conditions where the (clean) speech signal is accessible and can be modified prior to its presentation. However, many of these algorithms only consider the detrimental effect of noise and disregard the effect of reverberation. Therefore, in this paper we propose to additionally incorporate the detrimental effects of reverberation into noise-adaptive nearend listening enhancement algorithms. Based on the Speech Transmission Index (STI), which is widely used for speech intelligibility prediction, the effect of reverberation is effectively accounted for as an additional noise power term. This combined noise power term is used in a state-of-the-art noise-adaptive NELE algorithm. Simulations using two objective measures, the STI and the short-time objective intelligibility (STOI) measure demonstrate the potential of the proposed approach to improve the predicted speech intelligibility in noisy and reverberant conditions. Index Terms: speech-in-noise enhancement, reverberation, speech intelligibility, near-end listening enhancement

In slotted random access of many nodes, multi-user detection (MUD) can be applied to handle collisions. One novel PHY layer approach for jointly detecting activity and data in such a setting is Compressed Sensing based Multi-User Detection (CS-MUD). In this paper, we first summarize previous investigations on CS-MUD and subsequently propose two novel solutions for problems which have not yet been fully addressed: Firstly, we improve on previous results, by introducing a new approach which incorporates the channel decoder into the Compressed Sensing (CS) detector. Secondly, we analyze the resource efficiency of CS-MUD by adapting phase diagrams known from CS literature to the application of sporadic communication.

In adaptive feedback cancellation the computational complexity and the convergence speed are determined by the number of adaptive parameters used to model the acoustic feedback path. Therefore it has been proposed to reduce the number of adaptive parameters by modeling the feedback path as the convolution of a time-invariant common part and a time-varying variable part. While previous approaches have modeled the common part either using only poles or using only zeros, in this paper we propose to use a common pole-zero model and present an iterative method to compute the common poles and zeros. Using measured acoustic feedback paths from a two-microphone behind-the-ear hearing aid it is shown that the proposed model enables either to increase the modeling accuracy given a fixed number of parameters of the variable part or to reduce the number of parameters of the variable part given a desired accuracy.

In this contribution, a new preprocessing algorithm to improve speech intelligibility in noise is proposed, which maintains the signal power before and after processing. The proposed Adapt- DRC algorithm consists of two time- And frequency-dependent stages, which are both functions of the estimated SII. The first stage applies a time- And frequency-dependent amplification, while the second stage applies a time- And frequency-dependent dynamic range compression (DRC). Experiments with a competing speaker (CS) and a speech-shaped noise (SSN) show an increase in speech intelligibility for a wide range of SNRs for four different objective measures that are correlated with speech intelligibility. Listening tests conducted within the framework of the Hurricane Challenge with 175 subjects confirm these findings and show improvements of up to 20.5% in intelligibility for SSN and 12.3% for CS. Copyright

The growing field of Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has been shown for a synchronous CDMA transmission that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). However, many practical applications show some degree of asynchronicity. In order to reduce transmitter complexity, we propose an enhanced CS MUD that detects the delay in addition to activity and data. This solves synchronicity issues for scenarios with a known maximum delay, without requiring signaling or pre-compensation of asynchronicity.

Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has already been shown that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). For this detection we propose a new generalized Group Orthogonal Matching Pursuit algorithm that allows the use of additional side information regarding the sparsity structure. As a specific example, we exploit the information of a sparsity-aware Viterbi decoder in an iterative feedback loop to improve the activity detection. Here, a significant improvement of the activity detection is already achieved by executing only a single additional detection and decoding step.

One challenging future application in digital communications is the wireless uplink transmission in sensor networks. This application is characterized by sporadic transmissions by a large number of sensors over a random multiple access channel. To reduce control signaling overhead, we propose that sensors do not transmit their activity states; instead sensor activity is detected at the receiver. As sensors have low activity probabilities, the multi-user vector is in general sparse. This enables Compressive Sensing (CS) detectors to perform joint Multi-User Detection (MUD) of activity and data, by exploiting the sparsity. Since sensors are either active or inactive for several symbol durations, block-wise CS detection can be applied to improve the activity detection. In this paper, we introduce blockwise greedy CS MUD, compare it to symbol-wise greedy CS MUD, and show that statistically independent channels for each symbol further improve the activity detection for block-wise CS detection. Herein, we use Code Division Multiple Access (CDMA) as a multiple access scheme.

In this paper, two particular instances of LT codes with short message blocklength k and maximum likelihood (ML) decoding are investigated, i.e., random linear fountain (RLF) codes and (nearly) check-concentrated LT codes. Both show an almost equally good performance. The focus of this paper will be on RLF codes, a type of LT codes whose generator matrices are constructed from independent Bernoulli trials and have a binomial check node degree distribution. A new simple expression for an upper bound on the bit erasure probability under ML decoding is derived for RLF codes with density Δ = 0.5, i.e., with check node degree distribution Ω(x) = 2-k(1+x)k. It is shown that RLF codes with a minimum density far less than 0.5 are equally well suited to achieve a certain bit erasure probability for a given reception overhead. Furthermore, a characteristic term from a general upper bound on the bit erasure probability under ML decoding is identified that can be used to optimise check node degree distributions. Its implications on the performance of LT codes are qualitatively analysed.