Band-limited Channels Research Articles

Innovative Firmware Update Method to Microcontrollers during Runtime

This article presents a new firmware update paradigm for optimising the procedure in microcontrollers. The aim is to allow updating during program execution, without interruptions or restarts, replacing only specific code segments. The proposed method uses static and absolute addresses to locate and isolate the code segment to be updated. The work focuses on Microchip’s PIC18F27K42 microcontroller and includes an example of updating functionality without affecting ongoing applications. This approach is ideal for band limited channels, reducing the amount of data transmitted during the update process. It also allows incremental changes to the program code, preserving network capacity, and reduces the costs associated with data transfer, especially in firmware update scenarios using cellular networks. This ability to update the normal operation of the device, avoiding service interruption and minimising downtime, is of remarkable value.

Non-Data-Aided SNR Estimation for Bandlimited Optical Intensity Channels

Powerful and reliable estimation of transmission parameters is an indispensable task in each receiver unit-not only for radio frequency, but also for optical wireless communication systems. In this context, the signal-to-noise ratio (SNR) plays an eminent role, especially for adaptive scenarios. Assuming a bandlimited optical intensity channel, which requires a unipolar waveform design, an algorithm for SNR estimation is developed in this paper, which requires no knowledge of the transmitted data. This non-data-aided approach benefits to a great extent from the fact that very long observation windows of payload symbols might be used for the estimation process to increase the accuracy of the result; this is in striking contrast to a data-aided approach based on pilot symbols reducing the spectral efficiency of a communication link. Since maximum likelihood, moment-based or decision-directed algorithms are not considered for complexity and performance reasons, an expectation-maximization solution is introduced whose error performance is close to the Cramer-Rao lower bound as the theoretical limit, which has been derived as well.

Optical filtering impairment monitoring based on model fusion for optical networks.

Reconfigurable optical add-and-drop multiplexer (ROADM) is a key element in optical networks. As several ROADMs are cascaded over long paths, the penalty induced by ROADM has become non-negligible due to the tight optical filtering. In this case, for efficient and reliable network planning and operation, accurate monitoring of optical filtering penalty is very important. In this paper, we propose a real-time optical filtering monitoring scheme based on model fusion. We combine an analytical model based on the digital communications theory of band-limited channels with linear equalization and a data-driven model implemented using artificial neural network (ANN). The scheme can achieve high accuracy and interpretability. Moreover, since the input features are extracted from configuration parameters and receiver digital signal processing (DSP), no additional devices are needed, which is attractive for practical deployment. Extensive simulations and experiments are conducted to investigate the performance of the scheme, and the results show the superior performance.

A Comparative Study of Speech Coding Techniques for Electro Larynx Speech Production

Speech coding is a method of earning a tight speech signals representation for efficient storage and efficient transmission over band limited wired or wireless channels. This is usually achieved with acceptable representation and least number of bits without depletion in the perceptual quality. A number of speech coding methods already developed and various speech coding algorithms for speech analysis and synthesis are used. This paper deals with the comparison of selected coding methods for speech signal produced by Electro Larynx (EL) device. The latter is a device used by cancer patients with their vocal laryngeal cords being removed. The used methods are Residual-Excited Linear Prediction (RELP), Code Excited Linear Prediction (CELP), Algebraic Code Excited Linear Predictive (ACELP), Phase Vocoders based on Wavelet Transform (PVWT), Channel Vocoders based on Wavelet Transform (CVWT), and Phase vocoder based on Dual-Tree Rational-Dilation Complex Wavelet Transform (PVDT-RADWT). The aim here is to select the best coding approach based on the quality of the reproduced speech. The signal used in the test is speech signal recorded either directly by normal persons or else produced by EL device. The performance of each method is evaluated using both objective and subjective listening tests. The results indicate that PVWT and ACELP coders perform better than other methods having about 40 dB SNR and 3 PESQ score for EL speech and 75 dB with 3.5 PESQ score for normal speech, respectively.

Double-differential coded M-ary direct sequence spread spectrum for mobile underwater acoustic communication system

This paper presents a double-differential coded M-ary direct sequence spread spectrum (DD-MSS) underwater acoustic communication system to overcome problems of both low signal-to-noise ratio and severe Doppler effect. The system is very efficient in the challenging band-limited underwater acoustic channel and has many applications, particularly in which the transmitter and the receiver are mobile. Given that the bit error rate (BER) performance is unaffected, this scheme results in a significant improvement in the data rate compared to direct sequence spread spectrum (DSSS) communication systems. The MSS system uses M pseudo-random codes to represent k bits of information and offers advantages of a higher spectral efficiency and a lower energy consumption than the DSSS system. Both simulations and experiments are performed, and the results are discussed in the paper for the performance evaluation of the proposed scheme. The results demonstrate a better BER performance using the DD-MSS in a mobile environment.

BER Analysis of Neural Equalizer in OFDM systems

Digital communication gives larger data capacity and best communication speed. The signal transmitted through the wireless channels in digital communication affected by nonlinearities like noise. Inter Symbol Interference (ISI) dominantly causes huge data loss. It is caused due to multipath propagation, band-limited channels noise. Equalization, a process of inducing inverse channel response to compensate the effects of ISI is used to combat these effects. Orthogonal Frequency Division Multiplexing (OFDM) system supports high data rates and offers resistance towards the channel effect. Adding equalizer to OFDM systems make it more robust.In real time situations the response of channel is not known in prior. So, adaptive equalizers are implemented to compensate channel effects with the help of pilot symbols which are used to estimate the channel response and equalize the signal accordingly. Minimum Mean Square Error (MMSE), Least Square (LS) are some of the existing algorithms used to implement the equalizers. These conventional algorithms require some statistical properties of channel which is not possible in real time scenarios. Also, Linear Transverse equalizer failed to provide satisfactory results in the case of Non Minimum Phase channel. Neural Networks is capable to perform this task and can be used as an alternative to these algorithms.Back Propagation Neural Networks (BPNN) and Deep Neural Networks (DNN) can achieve better results when compared to conventional algorithms. Here, Deep Neural Networks with Adam optimization is proposed to enhance the performance of equalization. DNN can be used in both linear and non-linear channels. Also, Adam optimization used with Back Propagation uses variable and adaptive learning rate which reduces slow convergence and increases the efficiency of neural network unlike stochastic gradient descent which uses fixed learning rate.In this work the performance of proposed equalizer in OFDM systems is analyzed over flat and frequency selective fading of Rayleigh channels. The performance is measured in terms of Bit Error rate over SNR range -30dB to 25 dB. The results are compared with that of MMSE and it is observed that over the mentioned range of SNR the proposed equalizer achieved acceptable results and has low Bit error rate than that of MMSE in both flat and frequency selective fading channels.

On the achievable rate of bandlimited continuous-time AWGN channels with 1-bit output quantization

We consider a real 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. We derive an approximate lower bound on the capacity by lower-bounding the mutual information rate for input signals with exponentially distributed zero-crossing distances, sine-shaped transition waveform, and an average power constraint. The focus is on the behavior in the mid-to-high signal-to-noise ratio (SNR) regime above 10 dB. For hard bandlimited channels, the lower bound on the mutual information rate saturates with the SNR growing to infinity. For a given SNR the loss with respect to the unquantized additive white Gaussian noise channel solely depends on the ratio of channel bandwidth and the rate parameter of the exponential distribution. We complement those findings with an approximate upper bound on the mutual information rate for the specific signaling scheme. We show that both bounds are close in the SNR domain of approximately 10–20 dB.

Optimal DoS attack schedules on remote state estimation under multi-sensor round-robin protocol

In this paper, we focus on the design of optimal schedules for denial-of-service (DoS) attacks to degrade the performance of a remote state estimator of a networked system, where measurements of un-computational sensors are transmitted through a band-limited wireless channel under the round-robin protocol. Since attacker only has limited energy to jam the network, the attacker has to determine whether to jam the channel or not at each time step. In this paper, we propose a general error cost function in which average error and terminal error only become its particular cases. Then we analyze the properties of evolution of estimation error covariance under different schedules and further determine optimal attack schedules with respect to our proposed error cost. Examples are provided to illustrate the design of the optimal attack schedules.

The Temporal Limits Encoder as a Sound Coding Strategy for Bilateral Cochlear Implants.

The difference in binaural benefit between bilateral cochlear implant (CI) users and normal hearing (NH) listeners has typically been attributed to CI sound coding strategies not encoding the acoustic fine structure (FS) interaural time differences (ITD). The Temporal Limits Encoder (TLE) strategy is proposed as a potential way of improving binaural hearing benefits for CI users in noisy situations. TLE works by downward-transposition of mid-frequency band-limited channel information and can theoretically provide FS-ITD cues. In this work, the effect of choice of lower limit of the modulator in TLE was examined by measuring performance on a word recognition task and computing the magnitude of binaural benefit in bilateral CI users. Performance listening with the TLE strategy was compared with the commonly used Advanced Combinational Encoder (ACE) CI sound coding strategy. Results showed that setting the lower limit to ≥200 Hz maintained word recognition performance comparable to that of ACE. While most CI listeners exhibited a large binaural benefit (≥6 dB) in at least one of the conditions tested, there was no systematic relationship between the lower limit of the modulator and performance. These results indicate that the TLE strategy has potential to improve binaural hearing abilities in CI users but further work is needed to understand how binaural benefit can be maximized.

On the Spectral Efficiency of Bandlimited 1-Bit Quantized AWGN Channels With Runlength-Coding

Given a requirement for very high data rates, 1-bit quantization could become a key component to increase the energy efficiency in sub-THz communication systems. We study the spectral efficiency of a discrete-time 1-bit quantized oversampled AWGN channel using runlength-encoded input signals and faster-than-Nyquist signaling. We derive a framework for obtaining bounds on the spectral efficiency without complex simulations. This includes bandlimited channels as well as channels with excess bandwidth. We observe for the lower bound on the spectral efficiency up to 1.53 and 2.23 bit/s/Hz per dimension for bandlimitation and w.r.t. the 95%-power containment bandwidth, respectively.

On the relationship between: L Boltzmann entropy, M Planck distribution, C Shannon information and the principle of particles indistinguishability

The relationship between the fundamental formulas of statistical physics and information theory is considered. It is shown that the M Plank distribution for blackbody radiation and the quantum analog of C Shannon’s capacity for a frequency band-limited classical-quantum channel have a common basis – the principle of particles indistinguishability.

A New Algorithm for Baseband Pulse Transmission over Band-Limited Channels for Wireless Automotive Communications

A New Algorithm for Baseband Pulse Transmission over Band-Limited Channels for Wireless Automotive Communications

Time-Limited Codewords over Band-Limited Channels: Data Rates and the Dimension of the W-T Space

We consider a communication system whereby T-seconds time-limited codewords are transmitted over a W-Hz band-limited additive white Gaussian noise channel. In the asymptotic regime as , it is known that the maximal achievable rates with such a scheme converge to Shannon’s capacity with the presence of degrees of freedom. In this work we study the degrees of freedom and the achievable information rates for finite values of . We use prolate spheroidal wave functions to obtain an information lossless equivalent discrete formulation and then we apply Polyanskiy’s results on coding in the finite block-length regime. We derive upper and lower bounds on the achievable rates and the corresponding degrees of freedom and we numerically evaluate them for sample values of . The bounds are asymptotically tight and numerical computations show the gap between them decreases as increases. Additionally, the possible decrease from in the available degrees of freedom is upper-bounded by a logarithmic function of .

A Joint Encryption, Channel Coding and Modulation Scheme Using QC-LDPC Lattice-Codes

We propose a new nonlinear Rao-Nam like symmetric key encryption scheme. In our design, we employ a specific type of coded modulation schemes namely quasi-cyclic low-density parity-check (QC-LDPC) lattice-codes which have low-complexity encoding and decoding algorithms. Due to the application of coded modulation schemes in our design, the proposed scheme performs encryption, encoding and modulation simultaneously. Therefore, we regard the proposed scheme as a joint cryptosystem . The proposed joint cryptosystem withstands all variants of chosen plaintext attacks applied on Rao-Nam like cryptosystems due to its nonlinearity. Moreover, some conditions implying the uniformity of the ciphertexts distribution are introduced through our analysis. Our scheme is efficient and admits small key size. These features are obtained due to several reasons including the quasi-cyclic form of the generator and the parity-check matrices of QC-LDPC lattice-codes, and the simple hardware structure for generating the permutation matrix, the intentional error vector and the nonlinear functions used in our design. The QC-LDPC lattice-codes facilitate high-rate transmission which is suitable for bandlimited AWGN channels. Our simulations indicate that QC-LDPC lattice-codes outperform the error performance of high-order coded modulation schemes based on QAM modulations. Hence, our scheme provides secure, reliable and efficient data transmission in bandlimited AWGN channels.

Investigation of frequency dependent nonlinearity on pulse amplitude modulation in bandlimited visible light communications

In this work, the nonlinear behavior on pulse-amplitude-modulation (PAM) and their impact on the performance of RGB light-emitting-diodes (LED) based visible light communications (VLC) is presented. Particularly, how the limited source bandwidth affects the nonlinearity of fully modulated RGB LEDs is investigated. It is found that when symbol-rate is higher than the channel bandwidth the nonlinearity gradually diminishes. It causes the symbol-rate dependent nonlinearity. When applying PAM in VLC links, a pre-distortion method is often applied to compensate for the nonlinearity. Due to this symbol-rate dependency, however, the conventional pre-distortion method has a limitation on linearizing the non-linear VLC channel, especially in the case of applying various symbol-rates according to the channel condition. Although it can be adaptively compensated by nonlinear equalizers, their complexity is considerably high. In this work, the characteristics of multi-level PAM scheme in a band-limited nonlinear channel is presented. Also, the feasibility of a low complex but efficient pre-distortion method derived from monotonically decreasing characteristics of the frequency dependent nonlinearity is investigated.

Object Tracking in Random Access Networks: A Large-Scale Design

We address a scenario in which networked sensor nodes measure the strength of the field generated by a number of moving objects and transmit their measurements to a fusion center (FC) in a random access manner for the final reconstruction of the objects’ trajectories. To ensure scalability over an arbitrary coverage area, we divide the total observation area into design units (DUs), each consisting of several sensing cells. An extended Kalman filter is assigned to each cell, while neighboring cells communicate with each other to exchange current status, thus allowing state fusion whereby the Kalman filters adjust (overwrite) their local estimates at the end of each updating interval. In this manner, provisions are made for the objects leaving and entering a DU, yielding a system design that is scalable across a large coverage area without an increase in complexity (size) of the Kalman filters. We provide a step-by-step procedure for designing the system, taking into account the fact that sensors communicate to the FCs using random access over band-limited and imperfect channels where packet loss is inevitable due to collisions as well as communication noise. In addition, we study different rate control schemes in which the FC instructs the sensors to increase or decrease their sensing (transmission) rate in accordance with the currently estimated object locations. Performance is evaluated through simulation, showing the effectiveness of the approach proposed in terms of the mean squared localization error and data throughput, and quantifying the effect of limited bandwidth and lossy communication.

Quality Controlled EMG Signal Compression using Linear and Non Linear Transforms

In today’s era of telemedicine, data and graphical records are required to be transmitted over noisy, power limited and band limited channels. The effective compression is the best alternate to save time and bandwidth. For Electromyogram (EMG) signal, that are huge in data size, must be compressed in such a way so that can be recovered with minimum alterations. This work focused on the tuneable method to compress EMG signals, with linear and non linear transforms. The analysis is based upon compression factor (CF) and percentage root mean square difference (PRD). The results helps to conclude that non linear transform method have precedence over the linear transforms for almost entire range of user defined PRD (UPRD)

Literature Review on BER Improvement of Adaptive Equalizer

Channel equalization is an important aspect in high speed digital communication required for efficient and reliable data recovery and reception when the data is transmitted over band-limited channel subjected to noise and interference. We investigate channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance. Due to the high complexity of nonlinear equalizers and poor performance of linear ones, to equalize highly difficult channels, we employ piecewise linear equalizers.

Squeezing out the last few bits from band-limited channels with entropy loading.

The past decade witnessed the stirring development of advanced optical modulations and digital signal processing, which have been pushing optical transmission systems towards the capacity limit. Recent research has sought to squeeze out the last few bits from bandwidth-limited optical channels. One straightforward path is to expand the signal spectrum beyond the bandwidth limit while keeping the single-carrier modulation, which inevitably induces huge inter-symbol interference. To cope with such penalty, sophisticated digital nonlinear equalization on single-carrier signals should be exploited to reduce the burden of the subsequent forward error corrections (FEC). On the other hand, a more instinctive capacity-approaching method for bandwidth-deficient channels is the well-known water-filling realized by multicarrier modulation. As its approximation, bit loading (BL) has been a well-established algorithm to maximize the bit rate of a discrete multitone (DMT) channel with fixed-rate FEC. Built on probabilistic constellation shaping (PCS), multicarrier entropy loading (EL) goes beyond BL by continuous source entropy adaptation and has proven its superiority over the single-carrier PCS counterpart. In this paper, we reveal the EL advantage over BL on both achievable information rate (AIR) and FEC, aiming to prove EL as the optimum capacity-approaching solution for bandwidth-limited channels with frequency-selective fading. In a 100G direct detection system with a bandwidth-deficient directly modulated laser, EL improves the AIR by 5%-10% over BL using identical FEC overhead. EL will be critical for short-reach interconnects dominated by low-cost optical components to squeeze out the last few bits from the bandwidth-constrained system.

Underwater laser communication with sloped pulse modulation in turbid water

In this article, we propose the demonstration of an underwater wireless laser communication link operating at data rates of up to 100 Mbps over a band-limited turbid water channel using a modified pulse position modulation scheme with a sloped pulse. The communication link uses an avalanche photodiode module as the receiver. The bit error rate of a 5.4 × 10−6 by 450-nm blue laser diode in turbid water with 1.4 Nephelometric Turbidity Unit was below the forward error correction limit. Our demonstrated underwater wireless laser communication system is simple to use and maintains a high data transmission rate.