With over-the-air latency on the order of 0.1ms an-ticipated in 6G systems, the practical design of Finite-Blocklength Coding (FBC) has the potential to achieve extremely low latency communications. For this purpose, we focus on a joint FBC scheme in multi-user downlink systems. With a requirement of extremely low latency, we jointly encode data bits of multiple users over their orthogonal channel resources. As a result, we obtain throughput gain of the downlink transmission by an enlarged blocklength of FBC. In particular, we first present the joint encoding design for multiple downlink users by a matrix-based method. Under the multi-user joint FBC scheme, we then formulate an Integer Programming (IP) problem to maximize the throughput of downlink users subject to an average constraint on transmission power. By converting the derived IP problem to a nonlinear bipartite matching problem, we finally present a unified algorithm to obtain the optimal power-constrained throughput within the low latency requirement.

Wireless networked control systems for the Industrial Internet of Things (IIoT) require low-latency communication techniques that are very reliable and resilient. In this article, we investigate a coding-free control method to achieve ultralow latency communications in single-controller-multiplant networked control systems for both slow- and fast-fading channels. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller’s power limit. Although the optimization problem is a nonconvex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. We numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the control performance (i.e., the cost function) of a plant, which shows that the coding-free method is superior in a practical range of signal-to-noise ratios.

While the notion of age of information (AoI) has recently been proposed for analyzing ultra-reliable low-latency communications (URLLC), most of the existing works have focused on the average AoI measure. Designing a wireless network based on average AoI will fail to characterize the performance of URLLC systems, as it cannot account for extreme AoI events, occurring with very low probabilities. In contrast, this paper goes beyond the average AoI to improve URLLC in a vehicular communication network by characterizing and controlling the AoI tail distribution. In particular, the transmission power minimization problem is studied under stringent URLLC constraints in terms of probabilistic AoI for both deterministic and Markovian traffic arrivals. Accordingly, an efficient novel mapping between AoI and queue-related distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem considering both long and short packets transmissions. Simulation results show over a two-fold improvement, in shortening the AoI distribution tail, versus a baseline that models the maximum queue length distribution, in addition to a tradeoff between arrival rate and AoI.

We propose in this paper a new coding scheme called twisted-pair superposition transmission (TPST). The encoding is to “mix together” a pair of basic codes by superposition, while the decoding can be implemented as a successive cancellation list decoding algorithm. The most significant features of the TPST code are its predictable performance that can be estimated numerically from the basic codes and its flexible construction in the sense that it can be easily adapted to different coding rates. To construct good TPST codes in the finite length regime, we propose two design approaches – rate allocation and partial superposition. By taking tail-biting convolutional codes (TBCC) as basic codes, we show by numerical results that the TPST codes can have near-capacity performance in the short length regime.

We propose a two-dimensional modified min-sum algorithm for the LDPC codes in the fifth generation (5G) networks standard to approach the error performance of the sum-product algorithm (SPA). In the proposed decoding algorithm, we adopt a partial self-correction method followed by message amplification to improve the reliability of the variable-to-check (V2C) messages. To further approach the performance of the maximum likelihood decoding for 5G short LDPC codes, we propose an enhanced quasi-maximum likelihood (EQML) decoding method. The proposed decoding method performs multiple rounds of decoding tests once the first decoding attempt fails, where the decoder inputs of the selected unreliable variable nodes are modified in each decoding test. A novel node selection method based on the sign fluctuation of V2C messages is proposed for the EQML decoding method. We also present a partial pruning stopping (PPS) rule to reduce the decoding complexity by deactivating part of the decoding tests once a valid codeword is found. A lower bound on the error performance is also derived by using the semi-analytical method. Simulation results show that the EQML decoding method outperforms the SPA with the same decoding complexity and other QML decoding methods, and it approaches the Polyanskiy-Poor-Verdú bound within 0.4 dB.

We propose a joint channel estimation and signal detection technique for the uplink non-orthogonal multiple access using an unsupervised clustering approach. We apply the Gaussian mixture model to cluster received signals and accordingly optimize the decision regions to enhance the symbol error rate (SER). We show that when the received powers of the users are sufficiently different, the proposed clustering-based approach with no channel state information (CSI) at the receiver achieves an SER performance similar to that of the conventional maximum likelihood detector with full CSI. Since the accuracy of the utilized clustering algorithm depends on the number of the data points available at the receiver, the proposed technique delivers a tradeoff between the accuracy and block length.

We introduce a new family of error correction codes based on sparse superposition called Quasi- Orthogonal} Sparse Superposition Codes (QO-SSC). The core of QO-SSC is a special structure of the SSC projection matrix which is obtained by concatenation of orthogonal submatrices. Concrete examples of QO-SCC constructions based on Zadoff- Chu sequences and Kerdock bent sequences are provided. A low-complexity iterative successive interference cancellation algorithm is proposed for QO-SSC decoding. Performance evaluations show that QO-SSC is an efficient solution for short- packet transmission at low signal-to-noise ratios, as it achieves lower block error rate than conventional coded modulation schemes, e.g., QPSK- modulated polar codes. As a further advantage, QO-SSC features a low PAPR when transmitted using OFDM waveforms.

We consider the design of efficient neural-network based algorithms, referred to as neural decoders, for decoding linear and non-linear block codes, such as Hamming and constant-weight codes, respectively. Our goal is to study the impact of the number of layers and the number of hidden nodes in each layer on the performance and generalization ability of neural decoders. Specifically, we want to find the minimum number of hidden layers and the minimum number of hidden nodes in each layer required to achieve or closely approach the performance of the optimal maximum-likelihood soft-decision (ML-SD) decoder for a given code. For the linear block codes studied, we find that when the block-length is n, a neural network with a single hidden layer with n hidden units is necessary and sufficient to achieve the performance of the ML-SD decoder. For the non-linear block codes studied, a single hidden layer with n nodes results in performance that closely approaches that of the ML-SD decoder. In both the cases, we find that training at a signal-to-noise ratio (SNR) of 0 dB gives fairly good generalization ability, meaning that the neural decoder trained at an SNR of 0 dB works well across a wide range of SNR values.

We consider the concatenation of a convolutional code (CC) with an optimized cyclic redundancy check (CRC) code as a promising paradigm for good short blocklength codes. The resulting CRC-aided convolutional code naturally permits the use of serial list Viterbi decoding (SLVD) to achieve maximumlikelihood decoding. The convolutional encoder of interest is of rate-1/ω and the convolutional code is either zero-terminated (ZT) or tail-biting (TB). The resulting CRC-aided convolutional code is called a CRC-ZTCC or a CRC-TBCC. To design a good CRC-aided convolutional code, we propose the distancespectrum optimal (DSO) CRC polynomial. A DSO CRC search algorithm for the TBCC is provided. Our analysis reveals that the complexity of SLVD is governed by the expected list rank which converges to 1 at high SNR. This allows a good performance to be achieved with a small increase in complexity. In this paper, we focus on transmitting 64 information bits with a rate-1/2 convolutional encoder. For a target error probability 10−4, simulations show that the best CRC-ZTCC approaches the random-coding union (RCU) bound within 0.4 dB. Several CRCTBCCs outperform the RCU bound at moderate SNR values.

We consider multi-input convolutional codes with unequal error protection (UEP) among the inputs. Using conventional metrics including the free input distance and free input sum weight as a measure for UEP degree, we design efficient and optimal algorithms to evaluate these metrics for any number of inputs. Next, we discuss several ambiguities arisen from using these metrics to estimate convolutional code UEP property and propose two new metrics, the average weight per path and the ant colony system UEP (ACS-UEP) index. The ACS-UEP index can accurately and consistently capture UEP properties over all ranges of SNR. Using these metrics, we propose an algorithmic process for designing convolutional codes with bounded external degree complexity to minimize a weighted sum of input error probabilities. We analyze the effectiveness of the design approach and show significant improvements over existing UEP convolutional code designing approach in terms of both the absolute and UEP input error protections. Our proposed algorithms for designing multi-input convolutional codes with UEP are flexible, adaptable, and provide high performance codes within the specified complexity.

Various industrial Internet of Things applications demand execution periods throughout which no communication failure is tolerated. However, the classical understanding of reliability in the context of ultra-reliable low-latency communication (URLLC) does not reflect on the time-varying characteristics of the wireless channel. In this article, we introduce a novel mission reliability and mission effective capacity metric that takes these phenomena medium into account, while specifically studying multiconnectivity (MC)-enabled industrial radio systems. We assume uplink short packet transmission with no channel state information at URLLC user (the transmitter) and sporadic traffic arrival. Moreover, we leverage the existing framework of dependability theory and provide closed-form expressions (CFEs) for the mission reliability of the MC system using the maximal-ratio combining scheme. We do so by utilizing the mean time to first failure, which is the expected time of failure occurring for the first time. Moreover, we also derive exact CFEs for second-order statistics, such as level crossing rate and average fade duration, showing how fades are distributed in fading channels with respect to time. Furthermore, the design throughput maximization problem under the mission reliability constraint is solved numerically through the cross-entropy method.

Ultra-reliable low latency communication (URLLC) is defined by the the third generation partnership project (3GPP) for the fifth generation (5G) mobile systems. In order to realize the stringent requirements of ultra-high reliability and low latency, the data block length needs to be small. However, most of the state-of-art channel codes are designed and optimized for long block length. Analog fountain code (AFC) is a capacity-approaching self-adaptive code on wireless channels with linear encoding/decoding complexity. In this paper, we study the optimization of AFC in the finite length regime. We first analyze the effect of average degree on decoding performance of AFC in the finite length regime. Then, based on the characteristic of weight coefficient in AFC, we proposed a joint design of weight optimization (WO) progressive edge growth (PEG)-AFC algorithm to improve the decoding performance in the finite length regime, named PEG-AFC algorithm. Simulation results show that the proposed PEG-AFC algorithm can achieve lower bit error rate (BER) in a wide range of signal to noise ratio (SNR).

Ultra-reliable and low-latency communications (URLLC) are considered as one of three new application scenarios in the fifth generation cellular networks. In this work, we aim to reduce the user experienced delay through prediction and communication co-design, where each mobile device predicts its future states and sends them to a data center in advance. Since predictions are not error-free, we consider prediction errors and packet losses in communications when evaluating the reliability of the system. Then, we formulate an optimization problem that maximizes the number of URLLC services supported by the system by optimizing time and frequency resources and the prediction horizon. Simulation results verify the effectiveness of the proposed method, and show that the tradeoff between user experienced delay and reliability can be improved significantly via prediction and communication co-design. Furthermore, we carried out an experiment on the remote control in a virtual factory, and validated our concept on prediction and communication co-design with the practical mobility data generated by a real tactile device.

Ultra-reliable and low-latency communications (URLLC) are considered as one of three key application scenarios in the 5th generation (5G) communication systems. It is very challenging to satisfy the ultra-low end-to-end (E2E) delay requirement, especially in long distance communication scenarios since the delay in backhauls and core networks could be tens of milliseconds. In this work, we aim to reduce the latency experienced by users through prediction and communication co-design, where the transmitter predicts it's future states and sends them to the receiver in advance. Considering that the prediction is not error free, we take into consideration prediction errors and the packet loss in communications when analyzing the reliability of the system. Then, we formulate an optimization problem that minimizes the required bandwidth to satisfy the E2E delay and reliability requirements of URLLC. With the proposed method, it is possible for users to achieve zero latency experience when the prediction time equals to the communication delay. Simulation results verify the effectiveness of the proposed method, and show that the tradeoffs among bandwidth, reliability, and latency can be fundamentally improved via prediction and communication co-design. The results are further evaluated by using practical motion data acquired from experiments of a real hardware device.

Ultra-reliable and low-latency (UR-LL) communications are considered as a key feature in many application scenarios. Particularly, the 5th generation (5G) cellular networks are the main candidate for the implementation of UR-LL communications, by exploiting short packet transmissions. On the other hand, energy-harvesting (EH) solutions are envisaged to provide sustainable and self-sufficient green networks. In this paper, the tradeoffs between reliability and packet latency in a multi-user random access UR-LL-EH-5G network with finite blocklength codes is studied, while incorporating energy- and data-causality constraints. Analytical derivations for reliability and packet latency are provided to investigate the effect of diversity transmission, and the number of resource blocks allocated to user equipments (UEs). The derived analytical expressions are then numerically evaluated to shed light on different tradeoffs.

Traditional retransmission protocols require the receiver to decode the entire packet before sending feedback signals, which may not be a viable solution for ultra-reliable and low-latency communications (URLLC) as it may cause a significant delay. To address this issue, early hybrid automatic repeat request (E-HARQ) has been proposed as an alternative to reduce the processing time at the receiver and send the feedback signal as quickly as possible. In this work, we present a framework for analyzing the performance of ARQ protocols considering URLLC requirements for finite block-length packet transmission and use it to evaluate the performance of E-HARQ from an information theory perspective, comparing it to simple ARQ. The results show that this new class of retransmission protocols can significantly improve the performance of URLLC systems.

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complxity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3 generation partnership project (3GPP) calibrated channels, showing the validity of our proposal.

To support Ultra-Reliable and Low Latency Communications (URLLC) is an essential character of the 5th Generation (5G) communication system. Unlike the other two use cases defined in 5G, e.g. enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC), URLLC traffic has strict delay and reliability requirement. In this paper, an analysis model for URLLC traffic is proposed from the generation of a URLLC traffic until its transmission over a wireless channel, where channel quality, coding scheme with finite coding length, modulation scheme and allocated spectrum resource are taken into consideration. Then, network calculus analysis is applied to derive the delay guarantee for periodical URLLC traffic. Based on the delay analysis, the admission region is found under certain delay and reliability requirement, which gives a lower bound on required spectrum resource. Theoretical results in the scenario of a 5G New Radio system are presented, where the SNR thresholds for adaptive modulation and coding scheme selection, transmission rate and delay, as well as admission region under different configurations are discussed. In addition, simulation results are obtained and compared with theoretical results, which validates that the admission region derived in this work provides a lower spectrum allocation bound.

To estimate the probability of an event, conventional Monte Carlo (MC) needs $100/P_{\mathrm {e}}$ simulation runs to attain a 10% precision, where $P_{\mathrm {e}}$ is the probability of the event. It therefore encounters difficulty in simulation-based evaluation of packet error rates for ultra-reliable communication under its stringent requirement. Many fast simulation techniques for evaluating the probability of rare events have been proposed. However, a more efficient method for coded communication systems that can adaptively exploit the code structure and concentrate the generated noise vectors to the error-prone regions is desirable. We propose a method which seeks to adaptively learn a certain optimal histogram of the noise vectors and generate the noise vectors accordingly. The said histogram is a one-dimensional function and hence is easy to work with. The adaptation mechanism is code-agnostic. Simulation with cyclic redundancy check-aided polar coding in additive white Gaussian noise shows an approximately 10-100 times speed-up compared to conventional MC.

To achieve ultra-low latency mobile networking, recent efforts to integrate virtual cell with open-loop communications and proactive network association suggest the facilitation of new technological paradigm, but the interference from different co-locating virtual cells is hard to handle. Open-loop transmissions make beam-forming/interference alignment infeasible due to the need of channel state information feedback. Multiuser detection (MUD) is therefore employed to address the downlink interference. We note that the bit error rate (BER) of maximum-likelihood MUD (ML-MUD) is sensitive to the modulation of the interference. As the interferer uses low-order modulation, the BER of desired signal can approach the ideal case without interference. But if the interferer adopts high-order modulation, the resultant BER is significantly degraded. Our study shows that, such modulation sensitivity can be eased by the multi-antenna technique. We also propose two methods to reduce the notorious computational complexity of MUD, particularly involving higher order modulations. The first scheme is termed as the reduced-computation ML-MUD (R-ML-MUD) that exploits the characteristic of downlink to shrink the ML solution space, consequently leading to lower detection complexity. The second scheme is a new projection receiver, called generalized linear minimum mean square error equalizer, resulting in notable signal-to-noise ratio gain over the conventional projection method. The simulation results indicate that the proposed schemes and their integration can achieve satisfactory BER performance.