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.

Reliable and Energy-Aware Job Offloading at Terahertz Frequencies for Mobile Edge Computing

In this paper,we co-design the transmission power and the offloading strategy for job offloading to a mobile edge computing(MEC)server at Terahertz(THz)frequencies.The goal is to minimize the communication energy consumption while providing ultra-reliable low end-to-end latency(URLLC)services.To that end,we first establish a novel reliability framework,where the end-to-end(E2E)delay equals a weighted sum of the local computing delay,the communication delay and the edge computing delay,and the reliability is defined as the probability that the E2E delay remains below a certain pre-defined threshold.This reliability gives a full view of the statistics of the E2E delay,thus constituting advancement over prior works that have considered only average delays.Based on this framework,we establish the communication energy consumption minimization problem under URLLC constraints.This optimization problem is non-convex.To handle that issue,we first consider the special single-user case,where we derive the optimal solution by analyzing the structure of the optimization problem.Further,based on the analytical result for the single-user case,we decouple the optimization problem for multi-user scenarios into several sub-optimization problems and propose a sub-optimal algorithm to solve it.Numerical results verify the performance of the proposed algorithm.

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.

Resource allocation and hybrid prediction scheme for low-latency visual feedbacks to support tactile Internet multimodal perceptions

Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet(TI). However, most works omit the fact that different parts in an image may have distinct prediction requirements, based on which different prediction models can be used in the predicting process, and then it can further improve predicting quality especially under resources-limited environment. In this paper, a hybrid prediction scheme is proposed for the visual feedbacks in a typical TI scenario with mixed visuo-haptic interactions, in which haptic traffic needs sufficient wireless resources to meet its stringent communication requirement, leaving less radio resources for the visual feedback. First, the minimum required number of radio resources for haptic traffic is derived based on the haptic communication requirements, and wireless resources are allocated to the haptic and visual traffics afterwards. Then, a grouping strategy is designed based on the deep neural network(DNN) to allocate different parts from an image feedback into two groups to use different prediction models, which jointly considers the prediction deviation thresholds, latency and reliability requirements, and the bit sizes of different image parts. Simulations show that, the hybrid prediction scheme can further reduce the visual experienced delay under haptic traffic requirements compared with existing strategies.

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.