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Radio frequency energy harvesting offers a promising solution to provide low power Internet of Things (IoT) devices with convenient and perpetual energy supply. In this work, we investigate the reliable performance of an energy-constrained transmitter communicating with a receiver over Nakagami-m channel, where the effects of transceiver hardware impairments and finite blocklength coding are jointly considered. Specifically, the communication link between the transmitter and receiver operates within the coverage of an existing wireless system, with radio frequency signal from the existing system serving as an energy signal for the transmitter while acting as an interference signal for the receiver. By utilizing the finite-blocklength information theory, we first derive average block error rate (BLER) and asymptotic average BLER in closed-form expressions, which enable us to quantify the extent of reliability loss. Then, we analyze effective throughput of the system, and determine the optimal blocklength that maximizes the effective throughput. Computer simulations are employed to validate the accuracy of our analytical findings, demonstrating the presence of an outage threshold solely due to hardware impairments. Furthermore, if transmission rate exceeds the outage threshold defined by the level of hardware impairments, reliable communication within the system under consideration cannot be achieved, regardless of the transmit signal-to-noise ratio (SNR).

In this paper, we evaluate short packet communication (SPC) for a cooperative system where one relay assists data transmission between one multi-antenna source and one single antenna destination with the presence of co-channel interference at the relay. Two transmission schemes, i.e., transmit antenna selection (TAS) scheme and beamforming scheme (BF), are considered. Based on metric of SPC, we derive the average block error rate (BLER) for the system in asymptotic and closed-form expressions for both schemes. In addition, a solution of optimal power allocation (OPA) to maximize end-to-end effective system throughput is proposed. Effects of parameters such as total transmit power, number of the source’s antennas, number of co-channel interference, and packet length on the performance of the system are evaluated. Finally, the results reveal that the performance of the OPA scheme outperforms that of the benchmark scheme, i.e., equal power allocation solution, in terms of BLER and the effective throughput for both the TAS and BF schemes. Moreover, the performance of the system reaches to saturation value with more antennas at the source. The findings indicate that the TAS and BF schemes have the same performance in terms of both the BLER and the effective throughput.

Tail-biting convolutional codes (TBCCs) have become a research hotspot in the short-block-length regime due to the growing interest in strong short packet com-munications with low latency and ultra-reliability. TBCCs can maintain the decoding performance without rate loss caused by the tailed bits in the traditional convolutional encoder, which also have good rate compatibility with bet¬ter decoding performance than those of the iterative scheme for short block length codes. In this paper, a search algorithm is proposed to construct a set of rate-compatible TBCCs (RC-TBCCs) with consistent good frame error rate (FER) performance for fixed information length at various code rates. The algorithm considers the minimum distance profile of TBCCs. A set of RC-TBCCs is constructed for code rates from 1/3 to 1/8. The simulation results show that the proposed RC-TBCCs are superior to the LTE standard RC-TBCCs at different code rates.

In this letter, we consider the federated learning (FL) problem in the presence of communication errors. We model the link between the devices and the central node (CN) by a packet erasure channel, where the local parameters from devices are either erased or received correctly by CN with probability $\epsilon $ and $1-\epsilon $ , respectively. We proved that the FL algorithm in the presence of communication errors, where the CN uses the past local update if the fresh one is not received from a device, converges to the same global parameter as that the FL algorithm converges to without any communication error. We provide several simulation results to validate our theoretical analysis. We also show that when the dataset is uniformly distributed among devices, the FL algorithm that only uses fresh updates and discards missing updates might converge faster than the FL algorithm that uses past local updates. [ABSTRACT FROM AUTHOR]

Low latency and a massive connection have become the requirements of energy internet wireless communication. Effective capacity analysis of non-orthogonal multiple access (NOMA) networks with short packets is of vital importance in energy internet communication planning and design. Low-latency communications are one of the main application scenarios in next-generation wireless networks. This paper focuses on the effective capacity of NOMA networks, where the finite blocklength, delay exponent, and transmission error probability are taken into account. New exact and asymptotic expressions of effective capacities are derived for arbitrarily ordered users with a finite blocklength. Based on the analytical results, the high Signal-to-Noise Ratio slopes of effective capacity in NOMA networks are carefully attained. The numerical results validate that (a) non-orthogonal users are capable of obtaining a larger effective capacity when the blocklength decreases, and that (b), as the value of the error probability and delay exponent increases, the effective capacity of non-orthogonal users worsens. [ABSTRACT FROM AUTHOR]

Tail-biting convolutional codes (TBCCs) have become a research hotspot in the short-block-length regime due to the growing interest in strong short packet communications with low latency and ultra-reliability. TBCCs can maintain the decoding performance without rate loss caused by the tailed bits in the traditional convolutional encoder, which also have good rate compatibility with better decoding performance than those of the iterative scheme for short block length codes. In this paper, a search algorithm is proposed to construct a set of rate-compatible TBCCs (RC-TBCCs) with consistent good frame error rate (FER) performance for fixed information length at various code rates. The algorithm considers the minimum distance profile of TBCCs. A set of RC-TBCCs is constructed for code rates from 1/3 to 1/8. The simulation results show that the proposed RC-TBCCs are superior to the LTE standard RC-TBCCs at different code rates. [ABSTRACT FROM AUTHOR]

In this paper, we study the resource allocation problem for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long-packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and an unavoidable decoding error rate. Thus conventional resource allocation based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we propose to use the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Building on that, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to transmission reliability and various practical constraints. Since the formulated problem belongs to a non-convex mixed integer nonlinear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. We further propose a low-complexity algorithm based on the principles of block coordinate descent (BCD) and concave-convex procedure (CCCP). Finally, the simulation results show that the proposed low-complexity algorithm can achieve similar performance as that of the optimal solution and outperform the other baseline schemes significantly. [ABSTRACT FROM AUTHOR]

Low latency and a massive connection have become the requirements of energy internet wireless communication. Effective capacity analysis of non-orthogonal multiple access (NOMA) networks with short packets is of vital importance in energy internet communication planning and design. Low-latency communications are one of the main application scenarios in next-generation wireless networks. This paper focuses on the effective capacity of NOMA networks, where the finite blocklength, delay exponent, and transmission error probability are taken into account. New exact and asymptotic expressions of effective capacities are derived for arbitrarily ordered users with a finite blocklength. Based on the analytical results, the high Signal-to-Noise Ratio slopes of effective capacity in NOMA networks are carefully attained. The numerical results validate that (a) non-orthogonal users are capable of obtaining a larger effective capacity when the blocklength decreases, and that (b), as the value of the error probability and delay exponent increases, the effective capacity of non-orthogonal users worsens.

Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users’ individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes. [ABSTRACT FROM AUTHOR]

Short packet communications (SPC) will play an important role in future Internet-of-Things networks. Conventional pilot-assisted transmission (PAT) needs significant overhead to obtain accurate channel state information (CSI) for further symbol detection and bit recovery, thereby reducing the spectral efficiency of the transmission. In this paper, a machine learning framework called Label-Assisted Transmission is proposed, in which the received signals are grouped into clusters through clustering algorithms and known labels are transmitted for cluster-symbol/bits mapping. This novel framework supports bit recovery directly without requiring the bit-symbol mapping information. When such mapping information is available, modulation constrained (MC) clustering algorithms are proposed, which exploit the unique characteristics of digital communication signals. For frequency-flat channels, this novel design needs only one known label regardless of the modulation size and the missing labels can be reconstructed using a proposed label reconstruction scheme. For frequency-selective channels with $L$ -tap time domain channel responses, only $L$ known labels are needed to reconstruct the missing labels if orthogonal frequency division multiplexing technology is adopted. The proposed clustering receiver works well even when the number of clusters is much larger than the number of received samples. The performance of the proposed framework is analyzed empirically through extensive simulations, which verify that the proposed scheme approaches the maximum likelihood detector with perfect CSI. [ABSTRACT FROM AUTHOR]