Joint Decoding Research Articles

Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

AbstractSparse code multiple access (SCMA) is a promising non‐orthogonal multiple access scheme for cellular Internet of things (IoT) due to its ability to support massive connectivity, grant‐free transmission and scalability. Inspired by the recent developments of deep learning for physical layer communications, we present a design of an uplink SCMA receiver using deep learning. We propose the use of recurrent neural networks (RNNs) for joint channel estimation and multiuser data detection of uplink SCMA under time‐varying Rayleigh channel using a single deep learning structure. The use of RNNs enables the receiver to learn the time correlation between the received samples with a very low pilot density and with low complexity. Compared with the conventional SCMA receiver, the simulation results show that the proposed deep learning‐based receiver can achieve BER performance similar to that of the conventional SCMA receivers (such as sparse pilot channel estimator and message‐passing algorithm detector) with very low pilot density and with much lower complexity. Moreover, the proposed SCMA receiver shows good resilience to small changes in the receiver speed (second‐order channel statistics) which enables the proposed deep learning receiver to work over a reasonable range of receiver speeds without parameter tuning. Fine tuning the network parameters to capture the variations of the channel, during online transmission using small data sets and small training period, is also checked and provides additional BER benefits.

Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch

Using off-the-shelf resources from resource-rich languages to transfer knowledge to low-resource languages has received a lot of attention. The requirements of enabling the model to achieve the reliable performance, including the scale of required annotated data and the effective framework, are not well guided. To address the first question, we empirically investigate the cost-effectiveness of several methods for training intent classification and slot-filling models from scratch in Indonesia (ID) using English data. Confronting the second challenge, we propose a Bi-Confidence-Frequency Cross-Lingual transfer framework (BiCF), which consists of “BiCF Mixing”, “Latent Space Refinement” and “Joint Decoder”, respectively, to overcome the lack of low-resource language dialogue data. BiCF Mixing based on the word-level alignment strategy generates code-mixed data by utilizing the importance-frequency and translating-confidence. Moreover, Latent Space Refinement trains a new dialogue understanding model using code-mixed data and word embedding models. Joint Decoder based on Bidirectional LSTM (BiLSTM) and Conditional Random Field (CRF) is used to obtain experimental results of intent classification and slot-filling. We also release a large-scale fine-labeled Indonesia dialogue dataset (ID-WOZ 1 ) and ID-BERT for experiments. BiCF achieves 93.56% and 85.17% (F1 score) on intent classification and slot filling, respectively. Extensive experiments demonstrate that our framework performs reliably and cost-efficiently on different scales of manually annotated Indonesian data.

Distributed totally decomposed cumulative Goppa coded-cooperative communication with optimized selection in the relay

In this paper, powerful induced-cyclic totally decomposed cumulative Goppa (TDCG) codes are selected and used for coded-cooperative diversity, which can fit wireless communication applications. Two TDCG codes employed at the source and relay nodes establish the joint design of the destination channel code. In the cooperation phase, we consider an optimized relay channel code which improves the overall bit-error-rate (BER) performance. Two methods of optimization are explored. The most practical method for optimization in terms of reduced processing complexity is proposed. We use a joint decoding which exploits all the resources gained by the cooperation scheme at the destination. The BER performance is evaluated through Monte-Carlo simulations with BPSK modulation and coherent detection over the flat Rayleigh fading channel. The proposed TDCG coded-cooperative diversity scheme exhibit a better BER performance than the non-cooperation scheme and other existing coded cooperation methods of similar code rates under similar channel conditions.

Joint Channel Estimation and Decoding for Underwater Acoustic Communication With a Short Pilot Sequence

With the advance of undersea communication technologies, the operation of large numbers of small submerged devices and their connection, such as the Internet of Underwater Things (IoUT), is becoming possible. Deployed over long periods of time, energy restrictions limit the application of underwater acoustic (UWA) communication, and thus, the peer-to-peer (P2P) communication packets should be small with minimum overhead dedicated to training symbols. On the other hand, the UWA communication in physical layer requires good equalization performance to manage the inevitably long intersymbol interference. In this article, we propose a semiblind joint channel estimation and decoding (JCED) approach for P2P UWA communication to allow efficient channel equalization despite the short training sequence. We formulate the JCED problem and solve it by passing messages between the raw acoustic observations, the nodes that characterize the connections of the channel's taps, and the information-bearing symbols within a turbo equalization framework. To initialize the prior of the channel's components, we employ the expectation maximization algorithm, assuming a Gaussian mixture model for the channel's taps. Extensive numerical simulation and results from a field experiment show that our approach outperforms state-of-the-art benchmarks in terms of the communication system's capability of performing well with a small number of pilot symbols.

Optimized Design of Distributed Quasi-Cyclic LDPC Coded Spatial Modulation.

We propose a distributed quasi-cyclic low-density parity-check (QC-LDPC) coded spatial modulation (D-QC-LDPCC-SM) scheme with source, relay and destination nodes. At the source and relay, two distinct QC-LDPC codes are used. The relay chooses partial source information bits for further encoding, and a distributed code corresponding to each selection is generated at the destination. To construct the best code, the optimal information bit selection algorithm by exhaustive search in the relay is proposed. However, the exhaustive-based search algorithm has large complexity for QC-LDPC codes with long block length. Then, we develop another low-complexity information bit selection algorithm by partial search. Moreover, the iterative decoding algorithm based on the three-layer Tanner graph is proposed at the destination to carry out joint decoding for the received signal. The recently developed polar-coded cooperative SM (PCC-SM) scheme does not adopt a better encoding method at the relay, which motivates us to compare it with the proposed D-QC-LDPCC-SM scheme. Simulations exhibit that the proposed exhaustive-based and partial-based search algorithms outperform the random selection approach by 1 and 1.2 dB, respectively. Because the proposed D-QC-LDPCC-SM system uses the optimized algorithm to select the information bits for further encoding, it outperforms the PCC-SM scheme by 3.1 dB.

Improving Information Freshness via Backbone-Assisted Cooperative Access Points

Information freshness, characterized by Age of Information (AoI), is important for sensor applications involving timely status updates. In many cases, the wireless signals from one sensor can be received by multiple access points (APs). This article investigates the average AoI for cooperative APs (Co-APs), in which they can share information through a wired backbone network. We first study a basic backbone-assisted Co-AP system where APs share only decoded packets. Experimental results on software-defined radios (SDRs) indicate that Co-AP significantly improves the average AoI performance over a single-AP system. Next, we investigate an improved Co-AP system, called Soft-Co-AP. In addition to sharing decoded packets, Soft-Co-AP shares and collects soft information of packets that the APs fail to decode for further joint decoding. A critical issue in Soft-Co-AP is determining the number of quantization bits that represent the soft information (each soft bit) shared over the backbone. While more quantization bits per soft bit improves the joint decoding performance, it leads to higher backbone delay. We experimentally study the average AoI of Soft-Co-AP by evaluating the tradeoff between the backbone delay and the number of quantization bits. SDR experiments show that when the number of sensors is large, Soft-Co-AP further reduces the average AoI by 12% compared with Co-AP. Interestingly, good average AoI performance is usually achieved when the number of quantization bits per soft bit is neither too large nor too small.

Density Evolution Analysis of the Iterative Joint Ordered-Statistics Decoding for NOMA

In this paper, we develop a density evolution (DE) framework for analyzing the iterative joint decoding (JD) for non-orthogonal multiple access (NOMA) systems, where the ordered-statistics decoding (OSD) is applied to decode short block codes. We first investigate the density-transform feature of the soft-output OSD (SOSD), by deriving the density of the extrinsic log-likelihood ratio (LLR) with known densities of the priori LLR. Then, we represent the OSD-based JD by bipartite graphs (BGs), and develop the DE framework by characterizing the density-transform features of nodes over the BG under the binary phase shift keying (BPSK) transmission. Numerical examples show that the proposed DE framework accurately tracks the evolution of LLRs during the iterative decoding, especially at moderate-to-high SNRs. Based on the DE framework, we further analyze the BER performance of the OSD-based JD, and the convergence points of the two-user and equal-power systems.

Bimodal Fusion Network with Multi-Head Attention for Multimodal Sentiment Analysis

The enrichment of social media expression makes multimodal sentiment analysis a research hotspot. However, modality heterogeneity brings great difficulties to effective cross-modal fusion, especially the modality alignment problem and the uncontrolled vector offset during fusion. In this paper, we propose a bimodal multi-head attention network (BMAN) based on text and audio, which adaptively captures the intramodal utterance features and complex intermodal alignment relationships. Specifically, we first set two independent unimodal encoders to extract the semantic features within each modality. Considering that different modalities deserve different weights, we further built a joint decoder to fuse the audio information into the text representation, based on learnable weights to avoid an unreasonable vector offset. The obtained cross-modal representation is used to improve the sentiment prediction performance. Experiments on both the aligned and unaligned CMU-MOSEI datasets show that our model achieves better performance than multiple baselines, and it has outstanding advantages in solving the problem of cross-modal alignment.

Learning Joint Detection, Equalization and Decoding for Short-Packet Communications

We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all “at once”, detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not only in a higher spectral efficiency, but also translates into the possibility of shorter messages compared to using a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline and our simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> =96 information bits. Finally, we practically evaluate and confirm the improved performance of the proposed system over-the-air (OTA) using a software-defined radio (SDR)-based measurement testbed.

Spectrally Efficient Uplink Cooperative NOMA With Joint Decoding for Relay-Assisted IoT Networks

We propose a spectrally efficient uplink cooperative relaying protocol with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> multiple half-duplex relay stations (RNs) for wireless Internet of Things (IoT) networks, where two IoT devices (IDs) and an access point (AP) cannot directly communicate with each other. The two IDs simultaneously transmit signals over the same spectrum resource using the nonorthogonal multiple access (NOMA) technique. In the proposed technique, at a certain time slot, a single RN is selected to send the received packets from two IDs in the previous time slot, while the remaining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K-1$ </tex-math></inline-formula> RNs receive new packets from two IDs. Thus, the proposed relaying protocol can send <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> packets from an ID to the AP during <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N+1$ </tex-math></inline-formula> time slots without a duty-cycle loss in conventional half-duplex relaying schemes. We call the proposed protocol a spectrally efficient cooperative NOMA. The main highlight of our results is that we mathematically derive a closed-form outage probability of the proposed protocol by considering inter-relay interference, assuming an optimal joint decoding technique instead of successive interference cancelation (SIC) decoding at each RN. To the best of our knowledge, this is the first theoretical result in the literature. Extensive computer simulations are used to validate the mathematical results. Furthermore, it is shown that the proposed protocol significantly outperforms the existing two-hop half-duplex cooperative NOMA technique and the SIC decoding scheme in terms of outage probability.

CoSS: leveraging statement semantics for code summarization

Automated code summarization tools allow generating descriptions for code snippets in natural language, which benefits software development and maintenance. Recent studies demonstrate that the quality of generated summaries can be improved by using additional code representations beyond token sequences. The majority of contemporary approaches mainly focus on extracting code syntactic and structural information from abstract syntax trees (ASTs). However, from the view of macro-structures, it is challenging to identify and capture semantically meaningful features due to fine-grained syntactic nodes involved in ASTs. To fill this gap, we investigate how to learn more code semantics and control flow features from the perspective of code statements. Accordingly, we propose a novel model entitled CoSS for code summarization. CoSS adopts a Transformer-based encoder and a graph attention network-based encoder to capture token-level and statement-level semantics from code token sequence and control flow graph, respectively. Then, after receiving two-level embeddings from encoders, a joint decoder with a multi-head attention mechanism predicts output sequences verbatim. Performance evaluations on Java, Python, and Solidity datasets validate that CoSS outperforms nine state-of-the-art (SOTA) neural code summarization models in effectiveness and is competitive in execution efficiency. Further, the ablation study reveals the contribution of each model component.

Design of LDPC Coded Multi-User Massive MIMO Systems With MMSE-Based Iterative Joint Detection and Decoding

Design of LDPC Coded Multi-User Massive MIMO Systems With MMSE-Based Iterative Joint Detection and Decoding

Exploiting Rateless Codes and Cross-layer Optimization for Low-power Wide-area Networks

Long communication range and low energy consumption are the two most important design goals of Low-power Wide-area Networks (LPWANs); however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. Although there are PHY-layer and link-layer approaches proposed to improve the performance of LPWAN, they either rely heavily on the hardware modifications or suffer from low data recovery capability, especially with bursty packet loss patterns. In this article, we propose a practical system, eLoRa, for COTS devices. eLoRa utilizes rateless codes and joint decoding with multiple gateways to extend the communication range and lifetime of LoRaWAN. To further improve the performance of LoRaWAN, eLoRa optimizes parameters of the PHY-layer (e.g., spreading factor) and the link layer (e.g, block length). We implement eLoRa on COTS LoRa devices and conduct extensive experiments on an outdoor testbed to evaluate the effectiveness of eLoRa. Results show that eLoRa can effectively improve the communication range of DaRe and LoRaWAN by 43.2% and 55.7% with a packet reception ratio higher than 60%, and increase the expected lifetime of DaRe and LoRaWAN by 18.3% and 46.6%.

Hadamard–Viterbi Joint Soft Decoding for MFSK Underwater Acoustic Communications

Multiple Frequency-Shift Keying (MFSK) has been used widely for underwater acoustic communications due to its low complexity and channel robustness. However, the traditional MFSK has the limitation of a low bit rate compared with coherent acoustic communication. To increase the bit rate, this study designs a new MFSK with the concept of orthogonal frequency division multiplexing (OFDM). We also adopt a channel-concatenated coding to resist the multipath interference and design the iterative joint decoding. The channel-concatenated coding consists of a Hadamard code and a convolutional code. Correspondingly, the iterative joint decoding uses the Hadamard–Viterbi joint soft decoding framework with a newly designed branch metric, which uses the Hadamard structure. As an important preprocessing link for a received signal, frame synchronization and Doppler compensation are also described in detail in this study. Simulations and experiments are conducted to show the effectiveness of the proposed MFSK underwater acoustic communications.

Multiple decoding for polar-coded chase combining HARQ

The polar-coded hybrid automatic repeat request (HARQ) scheme with chase combining (CC) has very low complexity and is easy to combine with other techniques such as the coded-modulation and space–time coding, etc. However, the CC-HARQ scheme usually suffers from error and throughput performance degradation. A multiple decoding CC-HARQ (MDCC-HARQ) scheme is proposed in this paper by providing extra decoding chances when the joint decoding fails. Specifically, new LLR-sequences are constructed from the received signals, which can be utilized for the extra decoding attempts. Simulation results show that the proposed MDCC-HARQ scheme can achieve better error-performance and higher throughput efficiency, while requires less retransmission when compared with the CC-HARQ scheme.

Sparse IDMA: A Joint Graph-Based Coding Scheme for Unsourced Random Access

This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low-complexity schemes for the unsourced setting, while also accounting for inherent randomness. Under joint message-passing decoding, the proposed scheme offers good performance at a low computational complexity. Findings are supported by numerical simulations, and results are compared to existing alternatives.

LF-SWIPT: Outage Analysis for SWIPT Relaying Networks Using Lossy Forwarding With QoS Guaranteed

We analyze the outage performance of a lossy forwarding (LF) relaying system with the simultaneous wireless information and power transfer (SWIPT) capability. In the system of LF with SWIPT (LF-SWIPT), a source broadcasts its message to both a relay and a destination. A relay node with SWIPT functionality harvests energy and decodes information from the source signal. The energy is split into two parts for information processing and message forwarding, respectively. For information processing, the relay attempts to decode the incoming source signal and forms an estimate. Unlike the existing decode-and-forward SWIPT system (DF-SWIPT), the estimate is always forwarded using the harvested energy. The destination performs joint decoding to recover the message with the signals received from both the source node and the relay node. We derive the outage probability for the LF-SWIPT system based on the theorem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source coding with side information</i> . The simulation results demonstrate that the proposed system achieves significant gains (around 1–2 dB) compared to the DF-SWIPT system. We further evaluate the impact of the distance and the power splitting (PS) strategy on the system performance using simulations. Finally, we build an optimization algorithm on the PS ratio by maximizing the admissible region from the theoretical perspective.

Joint Decoding in Downlink NOMA Systems With Finite Blocklength Transmissions for Ultrareliable Low-Latency Tasks

Future ultrareliable low-latency tasks in the Internet of Things require finite blocklength transmissions. The spectral efficiency of finite blocklength transmissions, by incorporating nonorthogonal multiple access (NOMA), can be significantly improved. In conventional NOMA systems, the successive interference cancelation (SIC) is employed for signal decoding, which is optimal for sufficient long blocklength transmissions. However, for finite blocklength transmissions, the joint decoding instead of SIC is optimal. Considering the joint decoding, we study the decoding error probability and power allocation factor optimization problem, which aims at maximizing the effective throughput at the central user under the minimum-required effective throughput constraint at the cell-edge user. We put forward a 2-D search method to find the globally optimal solution and a low-complexity alternating optimization method to find the locally optimal solution. It is illustrated that our proposed joint decoding scheme has the higher effective throughput than the conventional SIC scheme.

A C-RAN Feasibility Study

The number of mobile phone users today is growing dramatically, which will cause network jamming and require a substantial amount of bandwidth. Cloud radio access network (C-RAN) is thus one of the interesting technology alternatives to solve this issue. C-RAN communicates with users using dispersed Remote Radio Heads (RRHs), each of which has one baseband unit (BBU). RRH performs soft relaying and is connected to the BBU via high capability, low latency front-haul lines. Due to the exchange of raw I/Q samples between the RRHs and the BBU, the C-RAN architecture places a restriction on front-haulfront-haul bandwidth. In BBU, many methods, including joint decompression and decoding (JDD) and wyner-ziv coding, are utilised to increase capacity.

Joint parameter estimation and decoding in a distributed receiver

SummaryThis paper presents an algorithm for iterative joint channel parameter (carrier phase, Doppler shift, and Doppler rate) estimation and decoding of transmission over channels affected by Doppler shift and Doppler rate using a distributed receiver. This algorithm is derived by applying the sum‐product algorithm (SPA) to a factor graph representing the joint a posteriori distribution of the information symbols and channel parameters given the channel output. In this paper, we present two methods for dealing with intractable messages of the SPA. In the first approach, we use particle filtering with sequential importance sampling for the estimation of the unknown parameters. We also propose a method for fine‐tuning of particles for improved convergence. In the second approach, we approximate our model with a random walk phase model, followed by a phase tracking algorithm and polynomial regression algorithm to estimate the unknown parameters. We derive the Weighted Bayesian Cramer‐Rao Bounds for joint carrier phase, Doppler shift, and Doppler rate estimation, which take into account the prior distribution of the estimation parameters and are accurate lower bounds for all considered signal‐to‐noise ratio values. Numerical results (of bit error rate and the mean‐square error of parameter estimation) suggest that phase tracking with the random walk model slightly outperforms particle filtering. However, particle filtering has a lower computational cost than the random walk model‐based method.