Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs

Online Meta-Learning For Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.Comment: arXiv admin note: text overlap with arXiv:2103.1348

Meta-ViterbiNet:Online Meta-Learned Viterbi Equalization for Non-Stationary Channels

Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scenarios, DNN-based receivers should be periodically retrained in order to track temporal variations in the channel conditions. To this aim, frequent transmissions of lengthy pilot sequences are generally required, at the cost of substantial overhead. In this work we propose a DNN-aided symbol detector, Meta-ViterbiNet, that tracks channel variations with reduced overhead by integrating three complementary techniques: 1) We leverage domain knowledge to implement a model-based/data-driven equalizer, ViterbiNet, that operates with a relatively small number of trainable parameters; 2) We tailor a meta-learning procedure to the symbol detection problem, optimizing the hyperparameters of the learning algorithm to facilitate rapid online adaptation; and 3) We adopt a decision-directed approach based on coded communications to enable online training with short-length pilot blocks. Numerical results demonstrate that Meta-ViterbiNet operates accurately in rapidly-varying channels, outperforming the previous best approach, based on ViterbiNet or conventional recurrent neural networks without meta-learning, by a margin of up to 0.6dB in bit error rate in various challenging scenarios

Temporal silencing of an androgenic gland-specific insulin-like gene affecting phenotypical gender differences and spermatogenesis

Androgenic glands (AGs) of the freshwater prawn Macrobrachium rosenbergii were subjected to endocrine manipulation, causing them to hypertrophy. Transcripts from these glands were used in the construction of an AG cDNA subtractive library. Screening of the library revealed an AG-specific gene, termed the M. rosenbergii insulin-like AG (Mr-IAG) gene. The cDNA of this gene was then cloned and fully sequenced. The cysteine backbone of the predicted mature Mr-IAG peptide (B and A chains) showed high similarity to that of other crustacean AG-specific insulin-like peptides. In vivo silencing of the gene, by injecting the prawns with Mr-IAG double-stranded RNA, temporarily prevented the regeneration of male secondary sexual characteristics, accompanied by a lag in molt and a reduction in growth parameters, which are typically higher in males of the species. In terms of reproductive parameters, silencing of Mr-IAG led to the arrest of testicular spermatogenesis and of spermatophore development in the terminal ampullae of the sperm duct, accompanied by hypertrophy and hyperplasia of the AGs. This study constitutes the first report of the silencing of a gene expressed specifically in the AG, which caused a transient adverse effect on male phenotypical gender differences and spermatogenesis. (Endocrinology 150: 1278 -1286, 2009) E ver since it was first proposed as the source of a hypothetical masculinizing hormone in crustaceans, the androgenic gland (AG) has been studied thoroughly in many crustacean species. The consensus emerging from these studies is that the AG plays a unifying role in the bewilderingly varied sex differentiation mechanisms in crustaceans (1-5). The AG constitutes a feature unique to male crustaceans in that it is an organ regulating sex differentiation separated from the gametogenic organ (unlike the single organ of vertebrate species). This separation enables manipulation of sex differentiation without affecting the gonads (6). In decapod male crustaceans, there are two AGs, each attached to the ejaculatory region of a vas deferens. In research spanning several decades, the functioning of the AG was investigated in a number of crustacean species by following the morphological and physiological effects of AG removal or transplantation on primary and secondary sex characteristics. In the amphipod Orchestia gamarella, for example, bilateral AG ablation decreased spermatogenesis and prevented the development of secondary male characteristics (7). In the crayfish Procambarus clarkii, injection of AG extracts accelerated the development of external male characteristics (8). In the giant freshwater prawn, Macrobrachium rosenbergii, a degree of masculinization was recorded in AG-implanted females (9). In the same species, fully functional sex reversal from males to neo-females (10) and from females to neo-males (11) was achieved by bilateral AG ablation and transplantation, respectively. The possibility of sex reversal has economical implications for the farming of this sexually dimorphic species because males grow faster than females (12). It is currently widely accepted that the AG of decapod crustaceans secretes the hormone(s) responsible for male differentiation, with a high probability of such a hormone(s) being proteinaceous in nature (13). This premise is supported by a histological study in the shore crab Phachygrapsus crassipes Multicellular organisms express various insulin-like peptides differentially. The insulin-like peptides discovered in inverte- Abbreviations: AG, Androgenic gland; CHH, crustacean hyperglycemic hormone; dsRNA, double-stranded RNA; GFP, green fluorescent protein; hAG, hypertrophy and hyperplasia of the androgenic gland; Mr-IAG, Macrobrachium rosenbergii insulin-like androgenic gland gene; RNAi, RNA interference; T7P, T7 promoter site at the 5Ј of one primer; UTR, untranslated region; XO-SG, X-organ sinus gland complex

Spatially Variable Effects of Artificially-Created Physical Complexity on Subtidal Benthos

10.3389/fevo.2021.690413Frontiers in Ecology and Evolution969041