Uniquely Decomposable Constellation Group-Based Sparse Vector Coding for Short Packet Communications

Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.

Energy Minimization for Heterogenous Traffic Coexistence with Puncturing in Mobile Edge Computing-Based Industrial Internet of Things

Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.

A Collaborative Machine Learning Scheme for Traffic Allocation and Load Balancing for URLLC Service in 5G and Beyond

Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .

Novel MAC Layer Proposal for URLLC in IndustrialWireless Sensor Networks

Ultra-reliable and low-latency communications(URLLC) has become a fundamental focus of future industrial wireless sensor net-works(IWSNs). With the evolution of automation and process control in industrial environments, the need for increased reliabilityand reduced latencies in wireless communications is even pronounced. Furthermore, the 5G systems specifically target the URLLCin selected areas and industrial automation might turn into a suitable venue for future IWSNs, running 5G as a high speed inter-process linking technology. In this paper, a hybrid multi-channel scheme for performance and throughput enhancement of IWSNsis proposed. The scheme utilizes the multiple frequency channels to increase the overall throughput of the system along with theincrease in reliability. A special purpose frequency channel is defined, which facilitates the failed communications by retransmis-sions where the retransmission slots are allocated according to the priority level of failed communications of different nodes. Ascheduler is used to formulate priority based scheduling for retransmission in TDMA based communication slots of this channel.Furthermore, in carrier-sense multiple access with collision avoidance(CSMA/CA) based slots, a frequency polling is introducedto limit the collisions. Mathematical modelling for performance metrics is also presented. The performance of the proposed schemeis compared with that of IEEE802.15.4e, where the performance is evaluated on the basis of throughput, reliability and the num-ber of nodes accommodated in a cluster. The proposed scheme offers a notable increase in the reliability and throughput over theexisting IEEE802.15.4e Low Latency Deterministic Networks(LLDN) standard.

Network slicing based joint optimization of beamforming and resource selection scheme for energy efficient D2D networks

The integration of network slicing into a Device-to-Device(D2D)network is a promising technological approach for efficiently accommodating Enhanced Mobile Broadband(eMBB)and Ultra Reliable Low Latency Communication(URLLC)services.In this work,we aim to optimize energy efficiency and resource allocation in a D2D underlay cellular network by jointly optimizing beamforming and Resource Sharing Unit(RSU)selection.The problem of our investigation involves a Mixed-Integer Nonlinear Program(MINLP).To solve the problem effectively,we utilize the concept of the Dinkelbach method,the iterative weightedℓ1-norm technique,and the principles of Difference of Convex(DC)programming.To simplify the solution,we merge these methods into a two-step process using Semi-Definite Relaxation(SDR)and Successive Convex Approximation(SCA).The integration of network slicing and the optimization of short packet transmission are the proposed strategies to enhance spectral efficiency and satisfy the demand for low-latency and high-data-rate requirement applications.The Simulation results validate that the proposed method outperforms the benchmark schemes,demonstrating higher throughput ranging from 11.79%to 28.67%for URLLC users,and 13.67%to 35.89%for eMBB users,respectively.

Dynamic resource allocation schemes for eMBB and URLLC services in 5G wireless networks

The fifth generation(5G)of wireless networks features three core use cases,namely ultra-reliable and low latency communications(URLLC),massive machine type communications(mMTC),and enhanced mobile broadband(eMBB).These use cases co-exist in many practical scenarios and compete for the same set of time and frequency resources,resulting in a natural trade-off in their performance.In this paper,a network supporting both URLLC and eMBB modes of operation is studied.To guarantee the ultra low latency requirement of URLLC,a dynamic resource allocation scheme indicated by a two-dimensional bitmap is proposed.This approach is capable to achieve finer granularity as well as lower false cancellation rate compared to the state-of-the-art methods.A novel power control and indication method is also proposed to dynamically provide different power control parameters to the user equipment(UE),while guaranteeing the reliability requirement of URLLC and minimizing the impact to eMBB.In addition,we devise a dynamic selection mechanism(DSM)to accommodate diverse scenarios,which is empowered with load prediction to become more intelligent.Our extensive system-level simulation results for eMBB-URLLC co-existence scenarios showcase that the perceived throughput of eMBB UEs is increased by 45.3%,while about 13.3% more UEs are enjoying URLLC services with at most 84% transmit power savings compared to the state-of-the-art methods.