Binary Communication System Research Articles

A novel integrated signal structure of satellite navigation and communication system with plug-and-play capability

An integrated navigation/communication signal is proposed in our previous work. It considered Cyclic Code Shift Keying (CCSK) as a communication signal combined with traditional Global Navigation Satellite System (GNSS) signal, further the optimized cyclic shift set for communication signal was designed. Based on the previous research, a fuzzy judging algorithm is proposed to acquire the integrated signal in this paper. This algorithm adopts segmenting correlation domain to improve the integrated signal transmission robustness. Moreover, in order to solve incompatibility problem between the integrated signal and binary communication system, a strategy for optimizing cyclic shift set basing on dual-thresholds is proposed, in which interleaving coding is used to resist interference. The fault-tolerant rate of proposed algorithm is improved by about 87% compared with traditional algorithms. Based on the compatibility, the Bit Error Rate (BER) of the integrated signal decreases obviously, and the reliability of the communication system is improved greatly, benefiting from that interleaving technology and error correction coding is applied.

Evolution of sexually dimorphic pheromone profiles coincides with increased number of male-specific chemosensory organs in Drosophila prolongata.

Binary communication systems that involve sex‐specific signaling and sex‐specific signal perception play a key role in sexual selection and in the evolution of sexually dimorphic traits. The driving forces and genetic changes underlying such traits can be investigated in systems where sex‐specific signaling and perception have emerged recently and show evidence of potential coevolution. A promising model is found in Drosophila prolongata, which exhibits a species‐specific increase in the number of male chemosensory bristles. We show that this transition coincides with recent evolutionary changes in cuticular hydrocarbon (CHC) profiles. Long‐chain CHCs that are sexually monomorphic in the closest relatives of D. prolongata (D. rhopaloa, D. carrolli, D. kurseongensis, and D. fuyamai) are strongly male‐biased in this species. We also identify an intraspecific female‐limited polymorphism, where some females have male‐like CHC profiles. Both the origin of sexually dimorphic CHC profiles and the female‐limited polymorphism in D. prolongata involve changes in the relative amounts of three mono‐alkene homologs, 9‐tricosene, 9‐pentacosene, and 9‐heptacosene, all of which share a common biosynthetic origin and point to a potentially simple genetic change underlying these traits. Our results suggest that pheromone synthesis may have coevolved with chemosensory perception and open the way for reconstructing the origin of sexual dimorphism in this communication system.

A 1-pJ/bit 80-Gb/s $2^{15}-1$ PRBS Generator With a Modified Cherry–Hooper Output Driver

An 80-Gb/s $2^{15}-1$ pseudorandom binary sequence (PRBS) generator suitable for testing binary communication systems is presented. Implemented in a 130-nm SiGe process, the circuit is optimized for low power dissipation. For the core functionality, an energy efficiency of 1 pJ/bit is achieved, which is one of the best values among comparable published designs. In addition, the improvement of the output signal in time domain and frequency domain is targeted. The key factors affecting an ideal PRBS spectrum are identified and addressed from the system and circuit point of view. This results in spur suppression of higher than 40 dB, which is 15 dB better than previously reported works exceeding 40 Gb/s. As an output driver, a modified Cherry–Hooper amplifier is chosen. A parametrized design methodology for obtaining Bessel response is introduced and the amplifier is optimized for low power operation. Measurement results show a single-ended output signal with over 300-mV eye opening, 0.6-ps rms jitter, and a signal-to-noise ratio better than 10.5 at 80 Gb/s, which is the best-in-class output eye diagram for a PRBS generator, to the best of our knowledge.

Analytic performance investigation of signal level estimator based on empirical characteristic function in impulsive noise

In this paper, we evaluate the mean square error (MSE) performance of empirical characteristic function (ECF) based signal level estimator in a binary communication system. By calculating Cramér-Rao lower bound (CRLB) we investigate the performance of the ECF based estimator in the presence of Laplace and Gaussian mixture noises. We have derived an analytic expression for the variance of the ECF based estimator which shows that it is asymptotically unbiased and consistent. Simulation and analytic results indicate that the ECF based level estimator outperforms the previously proposed estimators in some signal to noise ratio (SNR) regions when the observation noise distribution is unknown.

Secrecy of Communications in Data Transmission by Impulses with Unknown Moments of Appearance and Disappearance

We carried out a comparative analysis of the algorithms for detecting a rectangular impulse against Gaussian white noise under either authorized or unauthorized access to the transmitted data. We presupposed that for data transmission the binary communication system is used and that the useful information in the data is whether the signal is present or absent. The case is that unauthorized access by the outsider takes place in the situation when the signal parameters are completely or partially unknown. We then define the degree of the transmitted data secrecy by the secrecy ratio determining how highly the threshold signal-to-noise ratio increases when there is the unauthorized access instead of the authorized one.

Optimal Stochastic Signaling Under Average Power and Bit Rate Constraints

The optimal stochastic signaling based on the joint design of prior distribution and signal constellation is investigated under an average bit rate and power constraints. First, an optimization problem is formulated to maximize the average probability of correct decision over the set of joint distribution functions for prior probabilities and the corresponding constellation symbols. Next, an alternative problem formulation, for which the optimal joint distribution is characterized by a randomization among at most three mass points, is provided, and it is shown that both formulations share the same solution. Three special cases of the problem are investigated in detail. First, in the absence of randomization, the optimal prior probability distribution is analyzed for a given signal constellation and a closed-form solution is provided. Second, the optimal deterministic pair of prior probabilities and the corresponding signal levels are considered. Third, a binary communication system with scalar observations is investigated in the presence of a zero-mean additive white Gaussian noise, and the optimal solution is obtained under practical assumptions. Finally, numerical examples are presented to illustrate the theoretical results. It is observed that the proposed approach can provide improvements in terms of average symbol error rate over the classical scheme for certain scenarios.

Minimization of Error in Two Diversity Channels with Uncorrelated Gaussian Variables in Binary Communication System

The performance of a binary digital system in the presence of an uncorrelated Gaussian variable is considered here. The mean square value or expectation of the uncorrelated variables has zero mean and square root of spread of the distributed function with respect to these variables is variance of the noise components. On the basis of linear combination of two received signals one have to detect the value of controlled parameter, k optimally so that the probability of error should be minimized in the total binary communication system.

Chaos Communication: A Case of Statistical Engineering

The paper gives a statistically focused selective view of chaos-based communication which uses segments of noise-like chaotic waves as carriers of messages, replacing the traditional sinusoidal radio waves. The presentation concerns joint statistical and dynamical modelling of the binary communication system known as “chaos shift-keying”, representative of the area, and leverages the statistical properties of chaos. Practically, such systems apply to both wireless and optical laser communication channels. Theoretically, the chaotic waves are generated iteratively by chaotic maps, and practically, by electronic circuits or lasers. Both single-user and multiple-user systems are covered. The focus is on likelihood-based decoding of messages, essentially estimation of binary-valued parameters and efficiency of the system is in terms of the probability of bit decoding error. The emphasis is on exact theoretical results for bit error rate, their structured approximations and engineering interpretations. Design issues, optimality of performance, interference and fading are other topics considered. The statistical aspects of chaotic synchronization are involved in the modelling of optical systems. Empirical illustrations from an experimental laser-based system are presented. The overall aim is to show the use of statistical methodology in unifying and advancing the area.

Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.

In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

Generation of balanced quadrature phase shift keyed sequences through guided scrambling

Balanced codes (also called DC-free codes) are widely used in binary communication systems to increase the likelihood of accurate symbol recovery with practical demodulators. Guided scrambling (GS) is recognised as a viable approach to efficiently generate DC-free binary sequences. In this study the authors extend the use of GS to generate balanced sequences of quadrature phase shift keyed (QPSK) symbols by using arithmetic from the ring of polynomials defined over the Galois field of four elements. In addition to ensuring adequate timing information and consistent decision thresholds to improve the performance of practical demodulators, balanced encoding of QPSK symbol sequences creates a null at DC in the spectrum of the equivalent complex baseband signal. This corresponds to a null at the centre frequency of the bandpass QPSK signal that enables the insertion of a pilot tone and avoidance of narrowband interference without filtering or distortion of the signal. The authors outline sufficient conditions for the generation of balanced GS QPSK sequences, and based upon these conditions the authors recommend scrambling polynomials and quotient selection criteria. The authors then present analytical and simulation results that confirm the generation of balanced sequences using this approach.

On the Performance of a Digital Chaos-Based Communication System in Noisy Channels

In recent decades, many papers describing communication systems based on chaotic signals have been published. However, their performance under non ideal conditions needs further investigation. This work evaluates the performance in terms of Bit Error Rate (BER) of a binary communication system, based on chaotic synchronization, when white gaussian noise is added to the transmitted signal. Furthermore, we propose an encoding function that allows controlling the trade-off between how apparent is the message in the transmitted chaotic signal and BER performance.

Soft-Metric-Based Channel Decoding for Photon Counting Receivers

We address photon-number-assisted, polarization- based, binary communication systems equipped with photon counting receivers. In these channels information is encoded in the value of polarization phase-shift but the carrier has and additional degree of freedom, i.e. its photon distribution, which may be exploited to implement binary input-multiple output (BIMO) channels also in the presence of a phase-diffusion noise affecting the polarization. Here we analyze the performances of these channels, which approach capacity by means of iteratively decoded error correcting codes. In this paper we use soft-metric- based low density parity check (LDPC) codes for this purpose. In order to take full advantage of all the information available at the output of a photon counting receiver, soft information is generated in the form of log-likelihood ratios, leading to improved frame error rate (FER) and bit error rate (BER) compared to binary symmetric channels (BSC). We evaluate the classical capacity of the considered BIMO channel and show the potential gains that may be provided by photon counting detectors in realistic implementations.

Optimal receiver antenna location in indoor environment using dynamic differential evolution and genetic algorithm

Abstract Using the impulse responses of these multipath channels, the bit error rate (BER) performance for binary pulse amplitude modulation impulse radio ultra-wideband communication system is calculated. The optimization location of receiving antenna is investigated by dynamic differential evolution (DDE) and genetic algorithm (GA) to minimize the outage probability. Numerical results show that the performance for reducing BER and outage probability by DDE algorithm is better than that by GA.

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.

Stochastic signaling in the presence of channel state information uncertainty

In this paper, stochastic signaling is studied for power-constrained scalar valued binary communications systems in the presence of uncertainties in channel state information (CSI). First, stochastic signaling based on the available imperfect channel coefficient at the transmitter is analyzed, and it is shown that optimal signals can be represented by a randomization between at most two distinct signal levels for each symbol. Then, performance of stochastic signaling and conventional deterministic signaling is compared for this scenario, and sufficient conditions are derived for improvability and nonimprovability of deterministic signaling via stochastic signaling in the presence of CSI uncertainty. Furthermore, under CSI uncertainty, two different stochastic signaling strategies, namely, robust stochastic signaling and stochastic signaling with averaging, are proposed. For the robust stochastic signaling problem, sufficient conditions are derived for reducing the problem to a simpler form. It is shown that the optimal signal for each symbol can be expressed as a randomization between at most two distinct signal values for stochastic signaling with averaging, as well as for robust stochastic signaling under certain conditions. Finally, two numerical examples are presented to explore the theoretical results.

Precise BER Computation for Binary Data Detection in Bandlimited White Laplace Noise

Investigation into the characteristics and behaviours of Laplace noise is of crucial importance for evaluating the performance of communication systems operating in impulsive noise, as well as for ultra-wideband wireless systems operating in the presence of multi-user interference. The bit error rate performances of a binary data communication system operating in the presence of additive bandlimited white Laplace noise is analyzed theoretically. A theoretical expression for the average bit error rate is derived using the Beaulieu series for the optimal soft-limiting detector and the matched-filter detector when arbitrary pulse shapes are used. The bit error rate performance of a reduced complexity version of the optimal detector, the hard-limiting detector, and a reduced complexity version of the matched-filter detector, the sum-of-samples detector, are also analyzed. The analytical expressions for the bit error rate are validated by numerical examples.

Performance of a Binary PPM Ultra-Wideband Communication System with Direct Sequence Spreading for Multiple Access

In this paper, we present an analysis of the BER performance of an ultra-wideband (UWB) system with pulse position modulation (PPM) for data modulation and direct sequence (DS) spreading for multiple access over indoor lognormal fading channels. A rake receiver is used to combine a subset of the resolvable multipath components using the maximal ratio combining technique. Inter-path and multiple-access interferences are modeled and incorporated into the bit-error-rate expressions. The analytical and simulation results allow one to quantify many critical aspects of a DS-PPM UWB system such as the gain of the optimally spaced signaling scheme over the orthogonal signaling scheme, the potential error floor given a specific channel multipath delay spread and the number of interfering users, tolerance of the system to timing jitter, and impact of user codes.

Optimal Stochastic Signaling for Power-Constrained Binary Communications Systems

Optimal stochastic signaling is studied under second and fourth moment constraints for the detection of scalar-valued binary signals in additive noise channels. Sufficient conditions are obtained to specify when the use of stochastic signals instead of deterministic ones can or cannot improve the error performance of a given binary communications system. Also, statistical characterization of optimal signals is presented, and it is shown that an optimal stochastic signal can be represented by a randomization of at most three different signal levels. In addition, the power constraints achieved by optimal stochastic signals are specified under various conditions. Furthermore, two approaches for solving the optimal stochastic signaling problem are proposed; one based on particle swarm optimization (PSO) and the other based on convex relaxation of the original optimization problem. Finally, simulations are performed to investigate the theoretical results, and extensions of the results to M-ary communications systems and to other criteria than the average probability of error are discussed.

On the Performance of Single-Threshold Detectors for Binary Communications in the Presence of Gaussian Mixture Noise

In this paper, probability of error performance of single-threshold detectors is studied for binary communications systems in the presence of Gaussian mixture noise. First, sufficient conditions are proposed to specify when the sign detector is (not) an optimal detector among all the single-threshold detectors. Then, a monotonicity property of the error probability is derived for the optimal single-threshold detector. In addition, a theoretical limit is obtained on the maximum ratio between the average probabilities of error for the sign detector and the optimal single-threshold detector. Finally, numerical examples are presented to investigate the theoretical results.

Data-Aided and Non-Data-Aided Estimation of Signal Amplitude for Binary Data Communication in Laplace Noise

The estimation of signal amplitude for a binary data communication system in the presence of Laplace noise is considered. Maximum likelihood estimators are derived for both data-aided and non-data-aided cases. An absolute median estimator with a simple implementation is also proposed. The performances of the proposed estimators are examined and compared to well-known previous estimators in terms of mean-squared error. Numerical results quantify the superiority of the proposed estimators over the conventional estimators.