Utilizing 1-bit quantization at the analog-to-digital converter (ADC) is a promising approach to reduce the problematically high power consumption of high resolution ADCs in millimeter-wave (mmWave) and sub-terahertz (THz) communications. However, as 1-bit quantization is a highly nonlinear operation standard channel estimation algorithms cannot be applied. Therefore, we study algorithms for channel estimation in receivers with 1-bit quantization under consideration of a two-wave with diffuse power (TWDP) fading channel model, which was shown to be a realistic model for indoor communications in the mmWave regime. We combine maximum-likelihood (ML) amplitude estimation with a least-squares (LS) phase estimation approach known from literature to estimate the fading channel based on blocks of pilot symbols periodically inserted into the transmit symbol sequence. Furthermore, we apply Wiener filtering for interpolation of the channel estimates at the data blocks. The estimation performance of the proposed algorithms is evaluated numerically in terms of the mean squared error (MSE) and the suitability of the approach is demonstrated by evaluating the coded block error rate (BLER) for an exemplary system in comparison to the case with perfect channel knowledge. Our results show that almost the same BLER can be achieved by utilizing the derived estimation approach as compared to a system with perfect channel knowledge.

A promising approach to reducing the energy consumption is to consider coarse quantization at the receiver. In this study, we investigate novel precoding techniques in space and time for bandlimited multiuser MIMO downlink channels with 1-bit quantization and oversampling at the receiver, considering zero-crossing modulation. The proposed time-instance zero-crossing modulation conveys the information into the time-instances of zero-crossings. Two design criteria for time-instance zero-crossing modulation are investigated, namely, the minimum distance to the decision threshold and the mean-square error between the received and the desired signal. The maximization of the minimum distance to the decision threshold can be formulated as a quadratically constraint quadratic program. As an alternative, an equivalent problem can be formulated based on power minimization, which reduces computational complexity. Departing from the conventional mean-square error based technique, a more sophisticated algorithm is developed, which implies active constellation extension in order to improve the performance at high SNR. The extended problem is solved with two approaches, namely by formulating the problem as a second-order cone program and by considering an alternating optimization algorithm. Numerical results show that the proposed time-instance zero-crossing precoding methods significantly improve the bit error rate compared to the state-of-the-art methods.

In this work, we consider the acquisition of stationary signals using uniform analog-to-digital converters (ADCs), i.e., employing uniform sampling and scalar uniform quantization. We jointly optimize the pre-sampling and reconstruction filters to minimize the time-averaged mean-squared error (TMSE) in recovering the continuous-time input signal for a fixed sampling rate and quantizer resolution and obtain closed-form expressions for the minimal achievable TMSE. We show that the TMSE-minimizing pre-sampling filter omits aliasing and discards weak frequency components to resolve the remaining ones with higher resolution when the rate budget is small. In our numerical study, we validate our results and show that sub-Nyquist sampling often minimizes the TMSE under tight rate budgets at the output of the ADC.

This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.

Wireless communications systems beyond 5G are expected to utilize large available bandwidths at frequencies above 100 GHz in order to achieve data rates above 100 Gbit/s. However, the power consumption of the analog-to-digital converters (ADCs) for such systems is becoming a major challenge. Trading a reduced amplitude resolution for an increased temporal resolution by employing temporal oversampling w.r.t. the Nyquist rate is a possible solution to this problem. In this work, we consider a wireless communications system employing zero-crossing modulation (ZXM) and 1-bit quantization in combination with temporal oversampling at the receiver, where ZXM is implemented by combining runlength-limited (RLL) transmit sequences with faster-than-Nyquist (FTN) signaling. We compare the performance and complexity of four different soft-output equalization algorithms, namely, two approximations of the linear minimum mean squared error (LMMSE) equalizer, a BCJR equalizer and a deep-learning based equalizer, for such systems. We consider the mutual information (MI) between the input bits of the RLL encoder and the output log-likelihood ratios (LLRs) of the RLL decoder as a performance measure and evaluate it numerically. Our results demonstrate that one of the proposed LMMSE equalizers outperforms the competing algorithms in the low and mid signal-to-noise ratio (SNR) range, despite having the lowest implementational complexity.

Wireless communications systems beyond 5G are foreseen to utilize the large available bandwidths above 100 GHz. However, the power consumption of analog-to-digital converters (ADCs) for such systems is expected to be prohibitively high, because it grows quadratically with the sampling rate for high amplitude resolutions. Shifting the resolution from the amplitude to the time domain, i.e., by reducing the amplitude resolution and by employing temporal oversampling w.r.t. the Nyquist rate, is expected to be more energy efficient. To this end, we propose a novel low-cost sub-terahertz system employing zero crossing modulation (ZXM) transmit signals in combination with 1-bit quantization and temporal oversampling at the receiver. We derive and evaluate new finite-state machines for efficient de-/modulation of ZXM transmit signals, i.e., for efficient bit sequence to symbol sequence de-/mapping. Furthermore, the coded performance of the system is evaluated for a wideband line-of-sight channel.

In this work, we investigate the spectral efficiency (SE) of a system where the transmitter utilizes zero crossing modulation (ZXM), which was implemented by combining faster-than-Nyquist signaling with runlength-limited transmit sequences, and the receiver employs 1-bit quantization and temporal oversampling. The SE is evaluated numerically with respect to a fractional power containment bandwidth, which allows for a fractional amount of out-of-band emissions. In contrast to most existing works, the SE is evaluated for a practical transmitter and receiver implementation under a wideband line-of-sight channel model. The studied system achieves SEs above 3 bit/s/Hz, which corresponds to an increase of more than 50 % as compared to standard QPSK. Furthermore, the studied system outperforms a related system by Deng et al., which also employs 1-bit quantization and temporal oversampling, by approx. 15 dB at a SE of 3 bit/s/Hz.

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.

Full-duplex (FD) jamming transceivers recently have been shown to enhance the information security of wireless communication systems by simultaneously transmitting artificial noise (AN) while receiving information. In this paper, we investigate whether FD jamming can also improve the system's secrecy energy efficiency (SEE) in terms of securely communicated bits per Joule when considering the additional power used for jamming and self-interference (SI) cancellation. Moreover, the degrading effect of the residual SI is also taken into account. In this regard, we formulate a set of SEE maximization problems for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, considering both cases where exact or statistical channel state information (CSI) is available. Due to the intractable problem structure, we propose iterative solutions in each case with a proven convergence to a stationary point. Numerical simulations indicate only a marginal SEE gain, through the utilization of FD jamming, for a wide range of system conditions. However, when SI can efficiently be mitigated, the observed gain is considerable for scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR), or for a bidirectional FD communication setup.

In this work we study the secrecy energy efficiency (SEE) of a multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, in terms of the securely communicated bits-per-Joule, where the legitimate receiver is equipped with full-duplex (FD) capability. In particular, we seek answer to the question: if and how the application of an FD jammer can enhance the system SEE, considering the additional power consumption used for jamming and self-interference cancellation, as well as the degrading effect of residual self-interference. In this regard, an SEE maximization problem is formulated. Due to the intractable problem structure, an iterative solution is provided with a guaranteed convergence to a local optimum. Moreover, the proposed solution is extended for a system with a bidirectional communication, where both legitimate nodes are equipped with FD capability. Numerical simulations indicate a marginal SEE gain, via the utilization of FD jamming, for a wide range of system conditions. However, the observed gain is significant for the scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR) condition or for a bidirectional FD communication setup, under the condition that the self-interference can be effectively and efficiently mitigated.

The study aims to reduce the complexity of autonomous walking robots by involving the human in the operation of the machine. By taking advantage of the intelligence of the operator, the complexity of bipedal locomotion was reduced to tractable problem. The result of this research is that a two-legged, single actuated degree of freedom, walking machine was designed, built, and tested. The robot successfully navigated terrain including steep inclines and stairs. Additionally, a mechatronic control system was added to provide for smooth speed control and regulation. The leg driving architecture, the double four-bar linkage, designed for this machine can be extended to produce simplified fourand six-legged walking machines. The preliminary results can be used to extend this concept to a machine with an actuated balance assist system based on the human operator's interaction with the machine.

Results of computational complexity exist for a wide range of phrase structure-based grammar formalisms, while there is an apparent lack of such results for dependency-based formalisms. We here adapt a result on the complexity of ID/LP-grammars to the dependency framework. Contrary to previous studies on heavily restricted dependency grammars, we prove that recognition (and thus, parsing) of linguistically adequate dependency grammars is NP-complete.