Branch Metrics Research Articles

Innovative non-contact r-r intervals estimation using viterbi algorithm with Squared Branch Metric (VSBM)

Non-contact heartbeat detection by using Doppler sensor is a critical component of remote health monitoring systems, enabling continuous and unobtrusive monitoring of an individual’s cardiovascular health. In this paper, we reported an innovative approach for non-contact heartbeat detection using the Viterbi algorithm, leveraging the distribution of the difference of two adjacent R-R intervals (RRIs). RRIs represent the time between successive peaks in the electrocardiogram (ECG) signal and are fundamental in analyzing heart rate variability, mental stress conditions and heart diseases. Numerous non-contact Doppler sensor-based methods have been proposed for heartbeat detection, leveraging the evaluation of RRIs without physical device attachment. However, challenges arise from unwanted peaks introduced by respiration and slight body movements, even when the subject remains motionless with normal breathing. This study presented an innovative approach for selecting heartbeat peaks utilizing the Viterbi algorithm with the squared difference of two adjacent RRIs as the Branch metric (BM). Our preliminary experiments reveal that the difference between two adjacent RRIs closely follows a Gaussian distribution. Building upon this observation, we considered the Viterbi algorithm with Squared Branch Metric (VSBM) to estimate the heartbeat accurately. To assess the accuracy of our peak selection method, we conducted experiments comparing it with two existing peak detection methods: (i) Doppler output after Low-Pass Filter (LPF)-based method and (ii) Spectrogram-based method. Our results demonstrate that the proposed VSBM method is effective to detect the heartbeat accurately for each peak detection method. Furthermore, we compared the performance of “Spectrogram + VSBM” outperforms the “Doppler output after LPF + VSBM” method by the Root-Mean-Square Error (RMSE) of RRIs.

KNN-Based ML Model for the Symbol Prediction in TCM Trellis Coded Modulation TCM Decoder

Machine Learning is a booming technology today. In a machine learning set of training, data is to be provided to the model for training and that model predicts the output. Machine Learning models are trained using a computer program known as ML algorithms.The new machine learning-based Transition Metric Unit (TMU) of 4D- 8PSK Trellis coded Modulation TCM Decoder is presented in this work. The classic Viterbi decoder's branch metric unit, or TMU, takes on a complex structure. Trellis coded Modulation (TCM) is a combination of 8 PSK modulations and Error Correcting Code (ECC). TMU is one of the complex units of the TCM decoder, which is essentially a Viterbi decoder. Similar to how the first Branch metric is determined in the straightforward Viterbi decoder, the TCM decoder performs this BM computation via the TMU unit. The TMU becomes challenging and uses more dynamic power as a result of the enormous constraint length and the vast number of encoder states.In the proposed algorithm innovative KNN (K nearest neighbours) based ML model is developed. It is a supervised learning model in which input and output both are provided to the model, training data also called the labels, when a new set of data will come the model will give output based on its previous set experience and data.Here we are using this ML model for the symbol prediction at the receiver end of the TCM decoder based on the previous learning. Using the proposed innovation, the paper perceives the optimization of the TCM Decoder which will further reduce the H/W requirements and low latency which results in less power consumption.

MetNet: A Novel Low-Complexity Neural Network-Aided Detection for Faster-Than-Nyquist (FTN) Signaling in ISI Channels

This paper studies the application of neural networks to Viterbi detection of FTN signals in an intersymbol interference (ISI) channel. The main contribution of this paper is to propose a receiver structure for detecting FTN signals in unknown static ISI channel. In particular, we propose a novel low-complexity neural network structure for calculating the branch metrics, and we explore its suitability for FTN signalling with channel uncertainty. We compare the proposed network, which we call the Metric Net (MetNet), to a benchmark neural network-based technique for metric calculation, the ViterbiNet, which was originally designed for ISI channels. The simulation results confirm that the MetNet outperforms the ViterbiNet, with two orders of magnitude lower complexity, and is much more resilient to channel uncertainty than the traditional Viterbi detector, which uses Euclidean distance for metric calculations. We further show that the MetNet exhibits robustness to being trained at mismatched SNR values and FTN pulse acceleration factors, meaning that the number of trained models required can be significantly reduced. Additionally, the results show that the proposed MetNet remains a favorable alternative at much higher levels of channel uncertainties. The results also reflect that we can generalize the MetNet to work with different channel models defined by different decaying factors. Finally, we show that we succeed in achieving a bandwidth efficiency gain of 33% due to FTN by using the MetNet in the presence of channel uncertainty.

Low Complexity Adaptive Detection of Short CPM Bursts for Internet of Things in 6G.

With the standardization and commercialization of 5G, research on 6G technology has begun. In this paper, a new low-complexity soft-input-soft-output (SISO) adaptive detection algorithm for short CPM bursts is proposed for low-power, massive Internet of Things (IoT) connectivity in 6G. First, a time-invariant trellis is constructed on the basis of truncation in order to reduce the number of states. Then, adaptive channel estimators, recursive least squares (RLS), or least mean squares (LMS), are assigned to each hypothetical sequence by using the recursive structure of the trellis, and per-survivor processing (PSP) is used to improve the quality of channel estimation and reduce the number of searching paths. Then, the RLS adaptive symbol detector (RLS-ASD) and LMS adaptive symbol detector (LMS-ASD) could be acquired. Compared to using a least-squares estimator, the RLS-ASD avoids matrix inversion for the computation of branch metrics, while the LMS-ASD further reduces the steps in the RLS-ASD at the cost of performance. Lastly, a soft information iteration process is used to further improve performance via turbo equalization. Simulation results and analysis show that the RLS-ASD improves performance by about 1 dB compared to the state-of-the-art approach in time-variant environments while keeping a similar complexity. In addition, the LMS-ASD could further significantly reduce complexity with a power loss of approximately 1 dB. Thus, a flexible choice of detectors can achieve a trade-off of performance and complexity.

Advanced nonlinearity equalizer with TC-NL-MLSE for transmitting beyond 200-Gb/s PAM-8 in IM/DD systems.

The severe band-limited effect resulted from the low-cost optical transceiver increases the channel memory length and the number of taps of the equalizers. Besides, the interaction of fiber dispersion and square-law detection introduce nonlinear distortions in intensity modulation and direct-detection (IM/DD) transmission systems. The serious band-limited effect and nonlinear distortions degrade the transmission performance and bring challenges to current equalizers for low-complexity implementation. In this paper, we propose a trellis-compression nonlinear maximum likelihood sequence estimation (TC-NL-MLSE) algorithm to compensate the linear and nonlinear distortions with lower complexity. In the TC-NL-MLSE, we introduce a polynomial nonlinear filter (PNLF) to partly compensate both the linear distortions and nonlinear distortions. Then, we establish a look-up-table (LUT) to calculate the nonlinear branch metric (BM). To simplify the calculation, two or three levels with the highest probabilities are selected according to decision thresholds for each symbol to compress the state-trellis graph (STG). This significantly reduces computational complexity on BM calculations especially for high-order modulations. We conduct experiments to transmit beyond the 200-Gb/s PAM-8 signal over 2-km standard single mode fiber (SSMF) at C-band. The TC-NL-MLSE outperforms the reduced-state MLSE with PNLF, and can reach the 7%-overhead hard-decision forward error correction threshold. Moreover, the TC-NL-MLSE reduces the complexity by 97% compared with standard LUT-MLSE, limiting the multipliers around 100 at the expense of only 0.2-dB receiver sensitivity penalty.

Improved Recording Performance With Writer Design and Joint Learning-Based Neural Network for Heat-Assisted Interlaced Magnetic Recording

Heat-assisted interlaced magnetic recording (HIMR) further increases the areal density with the interlaced track layout architecture compared to conventional heat-assisted magnetic recording. However, besides the 2-D inter-symbol interference (ISI) and thermal jitters, the transition curvatures due to the circular thermal profile cause severe erasures after write and obvious nonlinear distortions of readback signals. This deteriorates the recording performance severely. Especially the degrees of transition curvatures for the low temperature written track (LT) and high temperature written track (HT) are distinct, which makes the correction of transition curvature with only the writer design impossible. Correspondingly, a comprehensive method consisting of writer design and multitrack detection algorithm with joint learning-based neural network (JLNN) is proposed to mitigate the effects of transition curvature and 2-D ISI for both LT and HT of HIMR. First, a designed writer with splitting main poles produces the spatially nonuniform write field along the crosstrack direction to correct the transition curvature of LT. Then the multitrack detection with JLNN and improved Bahl–Cocke–Jelinek–Raviv (BCJR) detector (JLNN-IB) algorithm is studied to further mitigate the effects of residual transition curvatures, thermal jitters, and 2-D ISI for both HT and LT. Specifically, the readback signals of read head array and decoded log-likelihood ratio (LLR) outputted by the low-density parity check (LDPC) decoder are fed into JLNN to obtain the equalized signal and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> probability (AP) of three tracks. Then the equalized signal as well as AP of current track and corresponding soft bit estimations of adjacent tracks are embedded into the branch metrics of improved BCJR detector for data detection, and the LDPC decoder is cascaded for the error correction. The simulation indicates that at the recording density of 2.11 Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the proposed comprehensive writer design and JLNN-IB algorithm provide 9.2 and 9.7 dB signal to noise ratio (SNR) gains compared to the conventional 2-D neural network equalizer and 2-D linear equalizer under the uniform write field and circular thermal profile, respectively.

Advanced DSP Enabled C-Band 112 Gbit/s/λ PAM-4 Transmissions With Severe Bandwidth-Constraint

The performance of high-speed intensity modulation direct detection (IM-DD) transmissions is severely degraded by the linear inter-symbol interference (ISI), due to both bandwidth-constrained optoelectrical components and the chromatic dispersion (CD). Meanwhile, nonlinear ISI due to intensity modulation and square-law detection can further deteriorate the IM-DD system performance significantly. Here, we first propose three metrics to quantify those impairments in IM-DD transmissions. Then, we compare both the BER performance and computational complexity of different DSP schemes to enable short-reach high-speed IM-DD transmissions with severe bandwidth constraint, using a C-band PAM-4 experimental test-bed with an end-to-end 10-dB bandwidth of 17 GHz. Those DSP schemes include receiver-side feedforward equalization (FFE), receiver-side Volterra filter equalization (VFE), transmitter-side Tomlinson-Harashima precoding (THP) together with receiver-side FFE, transmitter-side THP together with receiver-side VFE, channel-shortening filter (CSF) together with maximum likelihood sequence estimation implemented with linear branch metrics (MLSE-LI-BM), and CSF together with MLSE implemented with nonlinear branch metrics (MLSE-NL-BM). Our experimental results indicate that, only the combination of CSF and MLSE-NL-BM enables a 112 Gbit&#x002F;s transmission over 5 km fiber at the KP4-FEC threshold, but with an increased computational complexity.

Detection Scheme With Joint Intertrack Interference and Media Noise Mitigations for Heat Assisted Interlaced Magnetic Recording

Heat assisted interlaced magnetic recording (HIMR) is a promising candidate for the next-generation of magnetic recording technology to further increase the area density beyond 1Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Specifically, the high temperature and low temperature tracks are written in an interlaced order to improve the recording performance. However, the inter-track interference (ITI), inter-symbol interference (ISI) and thermal jitters brought by the increased recording density and Curie temperature variations are severe in HIMR, which degrade the bit error rate (BER) performance obviously. In this study, we propose a multitrack detection scheme with joint intertrack interference and media noise mitigations. Here a multi-task neural network (MTNN) is designed to simultaneously predict ITI pattern and residual media noise, then the 2D variable equalizers corresponding to different ITI patterns are implemented and predicted residual media noises are embedded into the branch metrics of modified Bahl-Cocke-Jelinek-Raviv (BCJR) detector to mitigate ITI and whiten media noise. The simulation demonstrates that the proposed MTNN with variable equalizer and modified BCJR detector (MTNN+VE+MB) algorithm mitigates the ITI and media noise effectively. At the channel bit density of 3.10 Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , it provides 2.6 dB signal-to-noise ratio (SNR) gain compared to that of conventional 2D fixed equalizer with pattern dependent noise prediction detector (FE+PDNP) for the low temperature (LT) tracks with 4% Curie temperature variance.

A Modified Viterbi Equalization Algorithm for Mitigating Timing Errors in Optical Turbulence Channels

In high-speed optical wireless communication (OWC) systems, timing errors can blur the edges of the received symbols, defined by the deviation of a signal’s timing event from its intended occurrence. The timing errors introduce the inter-symbol interference (ISI) to the neighboring symbols. In order to mitigate the degradation, a maximum likelihood (ML) based Timing Error Viterbi Equalization (TEVE) algorithm is proposed, in which the probability density functions (PDF) of the channel conditions and the timing errors are priori information. The mathematical form of each branch metric (BM) is deduced by those statistics in different situations of neighboring signs of timing errors. The summations of BMs form the cumulative metrics (CM). By adopting the add-compare-select iterations, we are able to detect symbols with the largest CMs. The theoretical closed-form average bit error rates (BER) are also deduced and compared in the cases whether the TEVE algorithm is utilized. Experimental results indicate that our proposed method can reduce timing errors’ impairment significantly with an acceptable complexity.

Multitrack Detection With a Two-Dimensional Soft-Transition Assisted Multitask Neural Network for Heat-Assisted Interlaced Magnetic Recording

Heat-assisted interlaced magnetic recording (HIMR) further increases recording density compared to heat-assisted magnetic recording due to the interlaced track layout. However, the smaller circular thermal profile of the top low-temperature-written tracks results in severe transition curvatures, which causes nonlinear distortions of the readback signal and degrades the bit error rate (BER) performance. Moreover, the increased recording density causes severe two-dimensional (2-D) intersymbol interference (ISI) along both down track and cross track directions. To mitigate the effect of nonlinear distortion and 2-D ISI in an HIMR system, we model a 2-D soft-transition, information-assisted, multitask neural network and modified Bahl–Cocke–Jelinek–Raviv (BCJR) detector (2DST-MTNN-MB) algorithm to detect three tracks simultaneously. The readback signal and 2-D soft-transition information are fed into the multitask neural network to obtain equalized signals and soft bit estimates of three tracks simultaneously. Then, the signal of the current track and soft estimates of the side tracks are embedded into the branch metrics of the modified BCJR detector for data detection, and the low-density parity check decoder is cascaded for error correction. The simulation results show that the 2DST-MTNN-MB algorithm provides 5.0 dB signal-to-noise ratio gains with reduced computation complexity compared with a single-track, single-task neural network and one-dimensional BCJR detector algorithm for the low-temperature-written track at channel bit density of 3.51 Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , thereby narrowing the gap of BER performance between high- and low-temperature-written tracks.

Morphometric and Physiologic Modeling Study for Endovascular Occlusion in Pediatric Trauma Patients.

The use of the resuscitative endovascular balloon occlusion of the aorta (REBOA) device is expanding in adult trauma. Reports of its use in pediatric patients have been published, but no guidelines currently exist nor has it been Food and Drug Administration approved in pediatrics. This project develops a model to determine appropriate balloon inflation volumes in pediatric patients to guide potential use. Artificial aortas were three-dimensional (3D) printed using synthetic polymers. Segments were created based on aortic diameters from 289 pediatric trauma patients' computer tomography (CT) scans. These aortic segments were inserted into a circulatory system model featuring two branches to simulate abdominal and upper body perfusion (cerebral, cardiac, and upper extremities). Sonographic flow meters and pressure transducers were placed along the circuit, and measurements were recorded as a REBOA device was inflated in the aortic segment. A negative sigmoidal relationship was observed between device inflation and aortic flow occlusion, with the initial 50% of inflation causing a 10% reduction in flow, followed by a steep decline. With increasing inflation, distal aortic flow and pressure were found to have an inverse relationship with the upper body branch metrics. In conclusion, pediatric patients present with a range of vessel diameters that occlude at various REBOA balloon inflation volumes. This study provides a basis to establish initial inflation volumes for safe REBOA deployment in appropriate pediatric trauma patients.

Hierarchical Trellis Based Decoder for Total Variation Sequence Detection (TVSD) in Space-Time Trellis Coded (STTC) Wireless Image/Video Communication

This paper proposes a robust reconstruction scheme for image/ video transmission in space-time trellis coding (STTC) based MIMO wireless multimedia sensor networks (WMSN). The information bits of the image/ video stream are modulated using STTC prior to transmission over the MIMO wireless channel between the multimedia sensors and the cluster head. At the receiver, a novel total variation sequence detector (TVSD) is developed that employs an anisotropic total variation (TV) regularized cost function to leverage the bounded variation property of the image/ video stream, in addition to the coding and diversity advantages of the STTC, for improved image/ video recovery. For reconstruction, a novel bi-layered hierarchical trellis based modified Viterbi decoder is developed to decode the optimal bit sequence corresponding to the least TV cost function using appropriately modified node and branch metrics. Simulation results using several test images and video sequences demonstrate the improved reconstruction performance of the proposed scheme, especially in the low SNR regime (0 - 10 dB), in comparison to the conventional maximum likelihood (ML) decoder, which makes it ideally suited for implementation in practical WMSN.

A multistandard and resource-efficient Viterbi decoder for a multimode communication system

We present a novel standard convolutional symbols generator (SCSG) block for a multi-parameter reconfigurable Viterbi decoder to optimize resource consumption and adaption of multiple parameters. The SCSG block generates all the states and calculates all the possible standard convolutional symbols corresponding to the states using an iterative approach. The architecture of the Viterbi decoder based on the SCSG reduces resource consumption for recalculating the branch metrics and rearranging the correspondence between branch metrics and transition paths. The proposed architecture supports constraint lengths from 3 to 9, code rates of 1/2, 1/3, and 1/4, and fully optional polynomials. The proposed Viterbi decoder has been implemented on the Xilinx XC7VX485T device with a high throughput of about 200 Mbps and a low resource consumption of 162k logic gates.

Power-Efficient Viterbi Decoder Architecture and Field Programmeble Gate Arrays Fpga Implementation

A Viterbi decoder system comprises a convolutional encoder and Viterbi decoder. In general, the code words generated from the input series of convolutional encoder arrive at the decoder through a noisy channel; however, the channel noise can cause corruption of code words. The Viterbi decoder extracts the original input message from the corrupted data using the Viterbi algorithm based on the maximum likelihood principle. A Viterbi decoder mainly comprises four essential units: a branch metrics unit, add-compare-select unit, path metrics unit, and survivor-path memory unit. Related complex calculations are repeated in these units at each clock cycle. In this study, a power- and area-efficient Viterbi decoder architecture that also reduces the computational complexity is proposed. Initially, a hard-decision Viterbi decoder system architecture design for Very Large Scale Integration (VLSI) realization was fulfilled without any further improvement to compare the performance of fundamental and improved designs with respect to power consumption. The initial design constitutes an essential base for the improved power- and area-efficient Viterbi decoder architecture. The improvements were made to achieve the less complex and power-efficient architectural system design. The performance of the proposed architecture was tested by a fieldprogrammable gate array (FPGA) platform, and the results have been reported. The architectural design is described using the Verilog hardware description language for comparing the related tests and performance of FPGA platform.

The K-Best Sphere Decoding for Soft Detection of Generalized Spatial Modulation

Due to the interchannel interference and the varying active antennas in generalized spatial modulation (GSM), it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a novel algorithm based on the K-best sphere decoding for the soft detection of the GSM signal, which performs the breadth-first tree search in two stages sequentially. By exploiting the null space of the GSM channel, the inactive antenna searching is carried out antennawise in the first stage, in which a recursive algorithm for updating the QL decomposition (a decomposition of a matrix into a product of an orthogonal matrix and a lower triangular matrix) is proposed to calculate the branch metrics and an effective examination scheme is developed to prune those illegal or repetitive child nodes. Based on the available QL decomposition structure that has been updated recursively during the first stage, the second stage of the proposed detector is then readily concatenated to search for the modulated symbols carried on the active antennas in a one-by-one manner. Moreover, owing to the soft-input soft-output nature, the proposed detector can be applied in a turbo decoder for a coded GSM system. Both complexity analysis and simulation results corroborate the superiority of the proposed soft detector for the GSM system.

Near Optimal Viterbi Algorithm for Storage Channels With Linear Regressive Noise

This paper proposes a noise-predictive detection algorithm for intersymbol interference channels with linear regressive noise. A block factorization of the covariance matrix of the linear regressive Gaussian noise is used to derive the branch metrics. This algorithm exhibits near optimal performance. A generalization of this algorithm to signal dependent linear regressive noise is also presented and its performance improvement over conventional algorithms with comparable complexity is shown using simulation results.

Memory-Reduced Maximum A Posteriori Probability Decoding for High-Throughput Parallel Turbo Decoders

Wireless communication standards make use of parallel turbo decoder for higher data rate at the cost of large hardware resources. This paper presents a memory-reduced back-trace technique, which is based on a new method of estimating backward-recursion factors, for the maximum a posteriori probability (MAP) decoding. Mathematical reformulations of branch-metric equations are performed to reduce the memory requirement of branch metrics for each trellis stage. Subsequently, an architecture of MAP decoder and its scheduling based on the proposed back trace as well as branch-metric reformulation are presented in this work. Comparative analysis of bit-error-rate (BER) performances in additive white Gaussian noise channel environment for MAP as well as parallel turbo decoders are carried out. It has shown that a MAP decoder with a code rate of 1/2 and a parallel turbo decoder with a code rate of 1/3 have achieved coding gains of 1.28 dB at a BER of 10$$^{-5}$$-5 and of 0.4 dB at a BER of 10$$^{-4}$$-4, respectively. In order to meet high-data-rate benchmarks of recently deployed wireless communication standards, very large scale integration implementations of parallel turbo decoder with 8---64 MAP decoders have been reported. Thereby, savings of hardware resources by such parallel turbo decoders based on the suggested memory-reduced techniques are accounted in terms of complementary metal oxide semiconductor transistor count. It has shown that the parallel turbo decoder with 32 and 64 MAP decoders has shown hardware savings of 34 and 44 % respectively.

Single Antenna Interference Cancellation for GSM/VAMOS/EDGE Using &lt;inline-formula&gt; &lt;tex-math notation="TeX"&gt;$L_{p}$&lt;/tex-math&gt;&lt;/inline-formula&gt;-Norm Detection and Decoding

Different schemes for single antenna interference cancellation (SAIC) in the Global System for Mobile Communications (GSM) have been proposed so far. Most of these schemes work best in synchronous interference environments and are limited to Gaussian minimum-shift keying (GMSK) modulation. In this paper, we propose several modifications of the conventional GSM receiver, which target the harmful effects of asynchronous co-channel interference (ACCI). Based on the observation that the impairment caused by ACCI cannot be modeled as additive Gaussian noise, which was assumed for the derivation of the metric used in state-of-the-art receivers, we propose modeling the impairment as Generalized Gaussian noise (GGN). This leads to modifications of the conventional branch metrics of the reduced-state equalizer and the decoder employed at the receiver. Both proposed modifications are applicable to any linear modulation alphabet and do not require any modification of the GSM air interface. Hence, the proposed novel branch metrics are also applicable in Enhanced Data Rates for GSM Evolution (EDGE) systems which employ 8-ary phase-shift keying (8-PSK) modulation and where the SAIC algorithms tailored for GMSK cannot be used. Furthermore, we show that the proposed branch metric modifications also improve the performance of the new Voice Services over Adaptive Multiuser channels on One Slot (VAMOS) extension of GSM in ACCI environments. Based on simulation results, the performance of the proposed schemes is compared with that of an unmodified receiver employing the conventional Gaussian metric. We show that the proposed receivers outperform the unmodified receiver in ACCI environments and discuss the complexity entailed by the proposed branch metric modifications.

Ultra Low Power Consumption Military Communication Systems

New military communications require increased versatility for the transmission of a wide range of data, voice and images. There are many algorithms needed for communication like FFT (Fast Fourier Transform), DCT (Discrete Cosine Transforms), Viterbi coders and decoders, trellis techniques etc. Advances in VLSI technology continue to improve the quality of algorithm that can be implemented on a single die. But even though all these algorithms implementable on a single chip, they require large amount of computation and consequently require large die area and high levels of power dissipation. In wireless communication, Viterbi decoder which consumes more power plays an important role in communication applications. Viterbi decoder is used to decode the received data which is encoded using convolution codes, which has less probability of error compared to other coding techniques. In many digital systems like Viterbi decoder, multiplexers and XOR gates are the major building blocks, which are often designed using transmission gate logic. So, to reduce the power consumption and number of transistors count in the decoder design, the transmission gates are replaced with the pass transistors which contain only one NMOS transistor. Since the eliminated PMOS transistor is large in size, the capacitance in the circuit is reduced thereby reducing the power, size and increase in the speed. This technique is simulated using TANNER tool. The improved performance of Viterbi decoder using pass transistor logic is compared with that of the transmission gate logic and results were compared using TANNER tools. General Terms Viterbi decoder algorithm, The branch metric (BM) , The path metric, Tanner Tools.

Optimal Viterbi Based Total Variation Sequence Detection (TVSD) For Robust Image/Video Decoding In Wireless Sensor Networks

In this letter, we propose a novel scheme for robust reconstruction, based on total variation regularization, towards image/video communication in multimedia wireless sensor networks. We derive the optimal joint source channel decoder as the combination of a maximum likelihood cost function and an anisotropic total variation norm based regularization factor. The proposed scheme exploits the bounded variation (BV) property of images and is thus ideally suited for reconstruction. Subsequently, it is demonstrated that the trellis based Viterbi decoder can be employed for robust image reconstruction using modified total variation state and branch metrics. Simulation results for BTC and JPEG encoded image transmission demonstrate a superior reconstruction performance for the proposed scheme in comparison to conventional methods.