We focus on low-power spike-based sensor node communication where runlength-limited (RLL) encoding is applied to map the information to the timing of the spikes. In this type of sensor communication, often rare events need to be communicated such that spikes are transmitted rarely, and the detection of those spikes has to be performed in an energyefficient way. Generally, standard analog-to-digital converter (ADC) based detectors are employed at the receiver, which are always active even if there is no spike being transmitted, resulting in unnecessary power consumption. To mitigate this issue, this paper studies an integrate-and-fire (IF) circuit followed by a counter and pre-processor as an energy-efficient spike detector. It counts the number of fires in each symbol interval, which is further used by the pre-processing unit to map the fire count within each symbol interval into transmitted RLL symbols' loglikelihood ratios. We evaluate a lower bound on the mutual information (MI) rate and the bit error rate of the communication system using this spike detector. Numerical results indicate that the proposed spike detection enables to receive RLL encoded spike sequences at a significantly lower energy per communicated bit Eb than traditional ADC based spike detection and alternative low-power IF time encoding machine (IF-TEM) based detection methods. Moreover, the required Eb decreases when increasing the minimum runlength constraint jointly with the signaling rate. Furthermore, a comparative discussion on the power consumption between the IF circuit based analog-todigital conversions and standard ADCs is presented.

For very high bandwidth systems the use of a 1-bit analog-to-digital converter is a promising solution to keep the power consumption of terahertz receivers manageable. With 1-bit quantization being a highly non-linear operation, it is necessary to revise all signal processing algorithms, including synchronization algorithms. In this paper we focus on the estimation of an unknown deterministic frequency offset between the transmitter and the receiver. To this end, two different data-aided frequency offset estimators are presented which both operate with 1-bit quantization at the receiver and in the presence of additive white Gaussian noise, and which correspond to the existing weighted phase averager and the planar filtered estimator used in conventional receivers. Comparing the performance of the considered estimators, we find an interesting trade-off between the achievable estimation performance, the computational complexity, and the estimation range. The planar filtered estimator has a higher complexity and achieves theoretical performance bounds for low signal-to-noise ratios in comparison to the weighted phase averager. Performance at high signal-to-noise ratios is limited by an error floor for both estimators, which can be lowered by oversampling in the case of the planar filtered estimator.

Runlength-limited (RLL) sequences have recently gained attention as a means to cope with the self-introduced intersymbol interference (ISI) in communications systems employing faster-than-Nyquist (FTN) signaling, in particular in systems using zero-crossing modulation (ZXM). However, previous research has revealed a significant gap between the achievable rates in ZXM systems using i) sequences generated by practical RLL codes and ii) maxentropic RLL sequences, which cannot solely be explained by the lower code rate of practical RLL codes. In this paper, we give evidence that the assignment between bit sequences and RLL sequences is essential for the performance of an RLL code when transmission over noisy channels is considered. We propose an RLL coding scheme with a high degree of freedom for the selection of the assignment and present a simple algorithm for its optimization. We show that our codes outperform RLL codes published in the literature in terms of the achievable rate in ZXM systems.

Energy efficiency is a critical challenge for next-generation mobile networks. Especially as traffic demand grows, the energy per bit must decrease significantly. One promising solution is the Gearbox-PHY, which adaptively switches between modulation schemes and tailored radio front ends to maximize energy efficiency while delivering required data rates. In this regard, high spectral efficiency needs can be addressed with standard quadrature amplitude modulation, while low-power alternatives like impulse radio are employed for lower data rate scenarios, significantly reducing front end power consumption. While we considered the energy optimization for such a Gearbox-PHY in prior work, the specific focus of this paper is the consideration of the trade-off between oscillator power consumption and its phase noise. Using literature-based models and measurements for hardware power consumption, we demonstrate that embracing hardware impairments can lead to substantial energy savings of up to three orders of magnitude.

Modulation schemes that encode information in the time domain play an important role for energy-efficient communications at high and low spectral efficiencies. Verdú showed that a spectrally efficient operation of pulse-based modulation schemes with independent identically distributed symbols at low signal-to-noise ratios is only possible when the phase is used as an additional degree of freedom. In the present work, four maxentropic symbol sequences are considered that encode information in the time domain and make use of the phase to varying degrees. The spectral efficiency is evaluated for the transmission over a bandlimited additive white Gaussian noise channel where the output is quantized with the lowest resolution that allows the reconstruction of the input symbols in the noiseless case. Moreover, faster-than-Nyquist (FTN) signaling is considered. Our results show that using the phase only to provide redundancy already leads to a significantly increased spectral efficiency, a lower required energy per communicated bit, and allows to benefit from FTN signaling. This makes the previously proposed zero-crossing modulation and time-derived zero-crossing modulation, a specific kind of impulse radio, to energy-efficient contenders for wideband millimeter-wave communications and for the operation of lowpower wireless sensor nodes in the Internet of things, respectively.

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.

Digital receivers based on 1-bit quantization and temporal oversampling w. r. t. the transmit signal bandwidth are a promising solution for the design of energy-efficient communications systems in the millimeter-wave (mmWave) and sub-terahertz bands. However, off-the-shelf algorithms for channel estimation cannot be applied as 1-bit quantization is a highly non-linear operation. Phase noise (PN) in particular has a deteriorating effect on the communication performance at these high frequencies and, therefore, needs to be tracked and compensated at the receiver. In this context, we derive an analytical solution for a close approximation of the Bayesian Cramér-Rao bound for PN estimation in systems employing 1-bit quantization, which provides insights into the impact of various design parameters on the achievable estimation performance. Furthermore, we use the bound to benchmark the performance of two existing PN estimators, showing that one of these estimators performs close to the optimum.

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.

A promising approach to avoid the bottleneck of the analog-to-digital converter’s (ADCs) high power consumption at high sampling frequencies is to use only 1-bit quantization resolution. By using temporal oversampling at the receiver, a high resolution in the time-domain can be achieved, which can partly recover the losses in terms of rate caused by a reduced amplitude resolution. However, channel estimation and synchronization has to be performed on 1-bit quantized receive samples, which poses a new challenge.This work is concerned with the phase estimation of a 1-bit quantized system with phase noise in the low signal-to-noise ratio (SNR) range. A block-based least squares (LS) estimator, whose output is interpolated by a Kalman filter is presented to track the phase noise. To enhance the performance, especially for the case of a large spacing between pilot blocks, we study the Rauch-Tung-Striebel (RTS) algorithm. Both algorithms are adjusted to the system characteristics and bounds for the steady state performance are derived. These bounds, as well as numerical results show that the RTS algorithm achieves a lower error variance than the Kalman filter at the price of increased latency, as it is non-causal.

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard off the shelf receiver algorithms cannot be applied. In this paper we consider an unknown phase rotation and timing offset and a fully digital receiver. To reduce the non-linear behavior introduced by 1-bit quantization, we assume that the receiver applies uniform phase and sample dithering, which can be implemented by sampling at an irrational normalized intermediate frequency and with an irrational oversampling factor, respectively. Based on the least squares objective function we derive a typical digital matched filter receiver with a data- and timing-aided phase estimator and square time recovery based timing estimation. Our main contribution is to show that both estimators are consistent under very general assumptions, e.g., arbitrary colored noise and stationary ergodic transmit symbols. Performance evaluations are done via simulations and are compared against a numerically computable upper bound of the Cramér–Rao lower bound. For low signal-to-noise ratio the estimators perform well but for high signal-to-noise ratio they run into an error floor. The performance loss of the phase estimator due to decision-directed operation or estimated timing information is marginal.

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.

Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh and Nakagami-m fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever. Furthermore, we find that the correlation loss is independent of the degrees of freedom encapsulated by the Nakagami-m fading parameter.

As communication systems require ever higher bandwidths, designing digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth can reduce circuit complexity and lower the energy consumption. This is due to the fact that high resolution in time domain is less difficult to achieve than high resolution in amplitude domain. However, as 1-bit quantization is a highly non-linear operation, standard channel parameter estimation algorithms for digital receivers cannot be applied. In this paper we consider an unknown phase rotation and an unknown timing offset at the receiver. Furthermore, we assume that the receiver applies uniform phase and sample dithering, which can be implemented by sampling at an irrational normalized intermediate frequency and with an irrational oversampling factor, respectively. Starting from the least squares objective function, we derive a phase independent, non-data aided (NDA) timing estimator. Considering sample dithering, we prove that the estimator is consistent, even if the noise is colored due to oversampling w.r.t. the signal bandwidth. Moreover, we numerically evaluate the variance of the estimator and compare it to the Cramér-Rao lower bound. We find that the qualitative behavior is similar to the Oerder&Meyr estimator that is often used in the unquantized case and that oversampling can significantly improve the performance.

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.

UAVs require reliable, cost-efficient onboard flight state estimation that achieves high accuracy and robustness to perturbation. We analyze a multi-sensor extended Kalman filter (EKF) based on the work by Leutenegger. The EKF uses measurements from a MEMS-based inertial system, static and dynamic pressure sensors as well as GPS. As opposed to other implementations we do not use a magnetic sensor because the weak magnetic field of the earth is subject to disturbances. Observability of the state is a necessary condition for the EKF to work. In this paper, we demonstrate that the system state is observable - which is in contrast to statements in the literature - if the random nature of the air mass is taken into account. Therefore, we carry out an in-depth observability analysis based on a singular value decomposition (SVD). The numerical SVD delivers a wealth of information regarding the observable (sub)spaces. We validated the theoretical findings based on sensor data recorded in test flights on a glider. Most importantly, we demonstrate that the EKF works. It is capable of absorbing large perturbations in the wind state variable converging to the undisturbed estimates.

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.

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard receiver algorithms cannot be applied. Thus, we derive performance bounds for phase, timing, and frequency estimation in order to gain a deeper insight into the impact of 1-bit quantization and oversampling. We identify uniform phase and sample dithering as crucial to combat the effect of the non-linearity introduced by 1-bit quantization. Since oversampling results in noise correlation, a closed form of the likelihood function is not available. Thus, we study a system model with white noise by adapting the receive filter bandwidth to the sampling rate. Considering the aforementioned dithering, we obtain very tight closed form lower bounds on the Cramér-Rao lower bound (CRLB) in the large sample regime. We show that with uniform phase and sample dithering, all large sample properties of the CRLB of the unquantized receiver are preserved under 1-bit quantization, except for an signal-to-noise ratio (SNR) dependent performance loss that can be decreased by oversampling. Numerical computations show that the properties of the CRLB for white noise still hold for colored noise except that the performance loss due to 1-bit quantization is reduced.

Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever.

Designing digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth enables lower power consumption and a reduced circuit complexity compared to conventional amplitude quantization, since high resolution in time domain is less difficult to achieve than high resolution in amplitude domain. However, standard receiver synchronization algorithms cannot be applied, since 1-bit quantization is a highly non-linear function. This paper is a first step to understand the influence of 1-bit quantization on the estimation of the channel parameters (e.g., timing, phase, and frequency offset). We will derive the Fisher Information (FI) matrix of phase and frequency, considering a known timing error and white Gaussian noise. Moreover, we propose to apply a uniformly distributed phase dither at the receiver, prior to 1-bit quantization, in order to reduce the nonlinear effect. The same effect can be achieved in practice by sampling at a low intermediate frequency. We obtain analytical results for the FI matrix with uniform phase dithering at the receiver and derive tight closed form upper bounds for the low and high SNR case.

With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management.

Multi-connectivity (MCo) is considered to be a key strategy for enabling reliable transmissions and enhanced data rates in fifth-generation mobile networks, as it provides multiple links from source to destination. In this paper, we quantify the communication performance of MCo in terms of outage probability and throughput. For doing so, we establish a simple, yet accurate analytical framework at high signal-to-noise ratio (SNR), in which the number of links, the spectral efficiency, the path loss, and the SNR are incorporated, giving new insights into the potentials of MCo as compared with the single-connectivity (SCo). These are our main contributions: 1) finding the exact coding gain of the outage probability for parallel block-fading channels; 2) quantifying the performance improvement of MCo over SCo in terms of SNR gain; and 3) comparing optimal and suboptimal combining algorithms for MCo at the receiver side, namely joint decoding, selection combining, and maximal-ratio combining, also in terms of SNR gain. In addition, we apply our analytical framework to real field channel measurements and thereby illustrate the potential of MCo to achieve high reliability and high data rates in real cellular networks.

Multi-connectivity is considered to be key for enabling reliable transmissions and enhancing data rates in future wireless networks. In this work, we quantify the communication performance by the outage probability and the system throughput. We establish a remarkably simple, yet accurate analytical framework based on joint decoding to describe the outage probability and the system throughput depending on the number of links, the modulation scheme, the code rate, the bandwidth, and the received signal-to-noise ratio. To investigate the tradeoff between the outage probability and the system throughput we define two modes to either achieve low outage probabilities or high system throughput which we refer to as the diversity and the multiplexing mode, respectively. We then establish a rate-reliability tradeoff analysis based on time sharing between both modes.

Analog-to-digital conversion with high resolution in amplitude has a relatively high energy consumption in communication systems. A promising alternative to reduce the energy consumption is 1-bit quantization. Considering such a receiver, we design and analyze continuous phase modulation (CPM) schemes, which are favorable because of their bandwidth efficiency and their constant envelope. In this context, oversampling with respect to the symbol duration is promising because CPM signals are not strictly bandlimited and because it reduces the loss in achievable rate caused by the quantization. The additional degrees of freedom brought by oversampling can be exploited by higher order modulation schemes. A lower bound on the achievable rate is computed based on an auxiliary channel law. In a further step, we optimize the input distribution with an optimization strategy based on a Markov source model. For a specific example, we give upper bounds on the achievable rate and present a state-machine representation for sequences which are reconstructible at the receiver. Finally, the proposed approach has the advantage of a constant envelope enabling an energy efficient transmitter design while achieving only a slightly lower 90% power containment bandwidth efficiency than existing methods with 1-bit quantization and oversampling.

Multi-connectivity is considered to be key for enabling reliable transmissions in future wireless networks. Transmission reliability depends on the used combining algorithm such as joint decoding (JD), maximum selection combining (MSC), and maximum ratio combining (MRC). To compare the performance of these combining algorithms we derive their outage probabilities based on distributed source coding. The outage probability is analytically described depending on the number of links, the modulation scheme, the code rate, and the received signal-to-noise-ratios (SNR). We show that JD requires less transmit power than MRC and MSC to achieve a given target outage probability.

We consider a continuous-time bandlimited additive white Gaussian noise channel with 1-bit output quantization. On such a channel the information is carried by the temporal distances of the zero-crossings of the transmit signal. The set of input signals is constrained by the bandwidth of the channel and an average power constraint. Under a set of assumptions, we derive a lower bound on the capacity by lower-bounding the achievable rate for a given set of waveforms with exponentially distributed zero-crossing distances. We focus on the behaviour in the high signal-to-noise ratio regime and characterize the achievable rate depending on the available bandwidth and the signal-to-noise ratio.

We provide a novel analysis of Wald's sequential probability ratio test based on information theoretic measures for symmetric thresholds, symmetric noise, and equally likely hypotheses. This test is optimal in the sense that it yields the minimum mean decision time. To analyze the decision-making process we consider information densities, which represent the stochastic information content of the observations yielding a stochastic termination time of the test. Based on this, we show that the conditional probability to decide for hypothesis H1(or the counter-hypothesis H0) given that the test terminates at time instant k is independent of time k. An analogous property has been found for a continuous-time first passage problem with two absorbing boundaries in the contexts of non-equilibrium statistical physics and communication theory. Moreover, we study the evolution of the mutual information between the binary variable to be tested and the output of the Wald test. Notably, we show that the decision time of the Wald test contains no information on which hypothesis is true beyond the decision outcome.

We present a new lower bound on the differential entropy rate of stationary processes whose sequences of probability density functions fulfill certain regularity conditions. This bound is obtained by showing that the gap between the differential entropy rate of such a process and the differential entropy rate of a Gaussian process with the same autocovariance function is bounded. This result is based on a recent result on bounding the Kullback-Leibler divergence by the Wasserstein distance given by Polyanskiy and Wu. Moreover, it is related to the famous hyperplane conjecture, also known as slicing problem, in convex geometry originally stated by J. Bourgain. Based on an entropic formulation of the hyperplane conjecture given by Bobkov and Madiman we discuss the relation of our result to the hyperplane conjecture.

We analyze the capacity of a continuous-time, time-selective, Rayleigh block-fading channel in the high signal-to-noise ratio (SNR) regime. The fading process is assumed stationary within each block and to change independently from block to block; furthermore, its realizations are not known a priori to the transmitter and the receiver (noncoherent setting). A common approach to analyzing the capacity of this channel is to assume that the receiver performs matched filtering followed by sampling at symbol rate (symbol matched filtering). This yields a discrete-time channel in which each transmitted symbol corresponds to one output sample. Liang & Veeravalli (2004) showed that the capacity of this discrete-time channel grows logarithmically with the SNR, with a capacity pre-log equal to 1-Q/N. Here, N is the number of symbols transmitted within one fading block, and Q is the rank of the covariance matrix of the discrete-time channel gains within each fading block. In this paper, we show that symbol matched filtering is not a capacity-achieving strategy for the underlying continuous-time channel. Specifically, we analyze the capacity pre-log of the discrete-time channel obtained by oversampling the continuous-time channel output, i.e., by sampling it faster than at symbol rate. We prove that by oversampling by a factor two one gets a capacity pre-log that is at least as large as 1-1/N. Since the capacity pre-log corresponding to symbol-rate sampling is 1-Q/N, our result implies indeed that symbol matched filtering is not capacity achieving at high SNR.

In this paper, we consider one-bit output quantization of a discrete signal with m real signaling points subject to arbitrary additive noise. First, the capacity-achieving distribution is determined for the corresponding channel. For any fixed quantization threshold q it concentrates on the two most distant signaling points, hence leading to an interpretation as binary asymmetric channel. The direct proof of this result allows for an explicit form of the capacity as a function of threshold q. We characterize stationary points as candidates for optimal thresholds by a condition on the differential quotient of the derivative of the binary entropy function. In contrast to intuition, symmetry of the noise distribution does not ensure a unique optimum antipodal threshold.

In this work, a discrete-time stationary Rayleigh flat-fading channel with unknown channel state information at transmitter and receiver side is studied. The law of the channel is presumed to be known to the receiver. For independent identically distributed (i.i.d.) zero-mean proper Gaussian input distributions, the achievable rate is investigated. The main contribution of this paper is the derivation of two new upper bounds on the achievable rate with Gaussian input symbols. One of these bounds is based on the one-step channel prediction error variance but is not restricted to peak power constrained input symbols like known bounds. Moreover, it is shown that Gaussian inputs yield the same pre-log as the peak power constrained capacity. The derived bounds are compared with a known lower bound on the capacity given by Deng and Haimovich and with bounds on the peak power constrained capacity given by Sethuraman et al.. Finally, the achievable rate with i.i.d. Gaussian input symbols is compared to the achievable rate using a coherent detection in combination with a solely pilot-based channel estimation.

In many typical mobile communication receivers, the channel is estimated based on pilot symbols to allow for a coherent detection and decoding in a separate processing step. Currently, much work is spent on receivers which break up this separation, e.g., by enhancing channel estimation based on reliability information on the data symbols. In this paper, we evaluate the possible gain of a joint processing of data and pilot symbols in comparison to the case of a separate processing in the context of stationary Rayleigh flat-fading channels. Therefore, we discuss the nature of the possible gain of a joint processing of pilot and data symbols. We show that the additional information that can be gained by a joint processing is captured in the temporal correlation of the channel estimation error of the solely pilot-based channel estimation, which is not retrieved by the channel decoder in case of separate processing. In addition, we derive a new lower bound on the achievable rate for joint processing of pilot and data symbols. Finally, the results are extended to multiple-input multiple-output channels.

In this work, we derive a new upper bound on the achievable rate of stationary Rayleigh flat-fading channels with i.i.d. input symbols. The novelty lies in the fact that this bound is not restricted to peak power constrained input symbols like known bounds, e.g., in [1] or [2]. Therefore, the derived upper bound can also be used to evaluate the achievable rate with i.i.d. proper Gaussian input symbols, which are capacity achieving in the coherent case. The derivation of the upper bound is based on the prediction error variance of the one-step channel predictor.

We consider a stationary Rayleigh flat-fading channel with temporal correlation and a compactly supported power spectral density of the channel fading process. We assume that the channel state is unknown to both transmitter and receiver, while the law of the channel is presumed to be known to the receiver. Given a set of fixed signaling sequences, the optimum input distribution, with respect to the achievable rate, has the property of a constant Kullback-Leibler distance between the output distribution and a mixture of the output distributions conditioned on the different input sequences. Based on this, we determine the set of optimum input distributions for PSK signaling. In addition, we identify the special case of transmitting one pilot symbol to acquire a phase reference as being included in the set of optimum input distributions. We derive an integral expression for the capacity constrained to PSK signaling depending on the autocorrelation of the channel and the SNR. Evaluation of the asymptotic high SNR behavior shows a loss in the constrained capacity with respect to the case of perfect channel knowledge corresponding to at least one signaling dimension, i.e., the information transmitted by one symbol.

The high peak-to-average power ratio is a major drawback of OFDM systems. Many PAPR reduction techniques have been proposed in the literature, among them a method that uses a subset of tones that do not carry any data, but are modulated such that the PAPR of the resulting time domain signal is minimized. Another problem of OFDM systems is the high out-of-band power caused by the sidelobes of the modulated tones. The OBP can be reduced by modulating reserved tones at the edges of the occupied spectrum so that the sidelobes of the data carriers are reduced. In this paper, we propose to consider both optimization problems jointly. This way, the amount of PAPR and OBP reduction can be significantly enhanced in comparison to a system that performs two separate optimization steps. Furthermore, the joint reduction algorithm offers more flexibility, because the relative weighting of the two optimization criteria can easily be changed, resulting in a smooth trade-off curve.

In this paper we derive an enhanced power control algorithm, fitting into the up/down control scheme, as it is considered in the frequency division duplex (FDD) mode of the current 3GPP standard. Analysis of the classical up/down power control scheme unveils, that with increasing velocities the power control performance degrades, as the fixed step size power control is not able to track the channel fading properly. For the uplink we derive a nonlinear control algorithm generating the up/down power control commands accounting for the future of the channel fading process. Simulations show that this algorithm in combination with perfect future channel state information can partially mitigate the drawbacks of a fixed step-size up/down power control. A prerequisite for predictive power control is the acquisition of the future channel state information. In this paper we deduce a robust and adaptive structure for the prediction of the channel fading process in the context of a power controlled code division multiple access (CDMA) system based on least mean square (LMS) adaptation. Link level simulations show a signal to noise and interference ratio (SINR) gain in terms of the block error rate, enabling a decrease of the target SINR and thus leading to an enhanced spectral efficiency.

Phase locked loops (PLL) for RF carrier synthesis often employ oscillators that insert a considerable amount of time varying phase noise into the received signal. That noise must then be removed in the digital baseband receiver. This phase noise is an indivisible superposition of noise components from receiver and transmitter. Regarding systems with multiple transmit and receive antennas (MIMO) and if multiple PLL for carrier synthesis are used each of the superposed phase noise processes per transmit and receive antenna pair can be measured at the receiver. This paper provides a new scheme for high SNR scenarios that exploits spatial correlation between these overlaying phase noise processes at the receiver in order to improve estimation and compensation of the phase noise. Therefore the Wiener filter approach is applied.