Low-latency Transmission Research Articles

Low-Latency Design for Satellite Assisted Wireless VR Networks

Satellite assisted Wireless Networks (SaWNs) have commonly been regarded as an integral part of the next generation wireless communication systems, offering a highway to global connectivity and multi-domain interconnection. To realize low-latency transmission for virtual reality (VR) applications, this letter first establishes a downlink transmission framework in SaWNs, where satellite and femtocell are respectively responsible for outdoor and indoor communications. Then, by designing the user association and serving strategy, a delay-minimization problem is formulated and solved based on convex optimization and Q-learning methods. Simulation results are finally presented, which reveal that our proposed algorithm, compared with conventional benchmark algorithms can reduce significantly transmission delay and meet the VR latency requirement well.

Secure Computation Offloading for Marine IoT: An Energy-Efficient Design via Cooperative Jamming

In smart ocean, the Marine Internet of Things (M-IoT) is ubiquitously used for data acquisition, communication connections, task computation, and so on. However, high-efficient energy consumption and secure yet low-latency transmission are subject to IoT devices' computation capacity and transmit power limitation, as well as wireless communication's broadcast characteristics. In this paper, we investigate the secure computation offloading in M-IoT with the assistance of unmanned surface vehicles (USVs) subject to the eavesdropping attack. In particular, we consider a scenario in which USVs are firstly scheduled to set up a high-quality communication link to the high altitude platform (HAP) and then exploited to provide cooperative jamming for the communication security at the physical layer when the HAP is performing computation offloading. We jointly optimize the USVs' positions, the duration of data uploading, the workload offloaded by HAP, the HAP's transmission power, and each USV's jamming signal power to minimize the system-wise energy consumption for completing the total workloads under the latency constraint. Despite the non-convexity, we decompose the problem vertically into a top problem that optimizes the USVs' positions and a bottom problem that optimizes the other variables. The Polyblock outer Approximation and bisection Search based algorithm (PAS-Algorithm) is proposed to alternatively optimize the variables in the bottom problem. And the Code bAsed croSs Entropy algorithm (CASE-Algorithm) is proposed to obtain the suboptimal solution to the formulated joint optimization problem. Numerical results validate the accuracy and effectiveness of our algorithms as well as the performance of optimizing the total energy consumption compared with the benchmark algorithms

High-Speed and Low-Latency Transmission by Millimeter-Wave Digital Wireless System for Si-IGBT/SiC-MOSFET Driver Control

High-Speed and Low-Latency Transmission by Millimeter-Wave Digital Wireless System for Si-IGBT/SiC-MOSFET Driver Control

R-AQM: Reverse ACK Active Queue Management in Multitenant Data Centers

TCP incast has become a practical problem for high-bandwidth, low-latency transmissions, resulting in throughput degradation of up to 90% and delays of hundreds of milliseconds, severely impacting application performance. However, in virtualized multi-tenant data centers, host-based advancements in the TCP stack are hard to deploy from the operators’ perspective. Operators only provide infrastructure in the form of virtual machines, in which only tenants can directly modify the end-host TCP stack. In this paper, we present R-AQM, a switch-powered reverse ACK active queue management (R-AQM) mechanism for enhancing ACK-clocking effects through assisting legacy TCP. Specifically, R-AQM proactively intercepts ACKs and paces the ACK-clocked in-flight data packets, preventing TCP from suffering incast collapse. We implement and evaluate R-AQM in NS-3 simulation and NetFPGA-based hardware switch. Both simulation and testbed results show that R-AQM greatly improves TCP performance under heavy incast workloads by significantly lowering packet loss rate, reducing retransmission timeouts, and supporting 16 times (i.e., 60 to 1000) more senders. Meanwhile, the forward queuing delays are also reduced by 4.6 times.

Simple Bounds on Delay-Constrained Capacity and Delay-Violation Probability of Joint Queue and Channel-Aware Wireless Transmissions

The emerging Ultra-Reliable and Low-Latency Communication (URLLC) is expected to meet a hard or probabilistic delay constraint that plays a key role in Time Sensitive Networking (TSN) and Deterministic Networking (DetNet). In this paper, we are interested in simple bounds for delay-constrained wireless communications with deterministic or even random packet arrivals. More specifically, we present a sufficient condition and derive a legitimate data arrival rate, with which the bounded delay can be guaranteed deterministically with an average power constraint. Our derived results will show that the delay-bounded data rate increases with the average power and the tolerated latency normalized to the coherent time. Furthermore, we derive the upper and lower bounds of the delay-violation probability (DVP) when the bounded delay condition cannot be satisfied. It is revealed that in the log coordinate, the DVP as a function of the normalized tolerated latency may enjoy a non-linear decay, with a decay rate that increases with the latency bound. This is in contrast to the large deviation based performance analysis that focuses on the low-latency transmission policies achieving linearly decayed DVP only. Our result demonstrates that the joint channel-aware and queue-aware scheduling may significantly reduce the DVP, compared to the single-layer approaches.

TBMOR: A lightweight trust-based model for secure routing of opportunistic networks

Opportunistic networks are self-organizing dynamic networks that use the communication opportunities presented by node movement to enable data delivery. Due to the indirect nature of inter-node connections and dynamic topology, opportunistic networks are vulnerable to malicious attacks, which poses a great challenge to their security. In this paper, we first propose a trust-based model for the secure routing of opportunistic networks (TBMOR), which incorporates forwarding positive degree (FP) and node activity degree (NA) into the calculation of trust value, evaluates nodes in the network by a dynamic combination of direct trust and indirect trust, and then optimizes the next node list using trust values and pruning strategies. Secondly, this paper extends the network life cycle by dynamically setting up energy thresholds to reduce network redundancy by dynamically partitioning the number of replicas using trust values.Simulation results show that compared to other routers, TBMOR can effectively resist malicious attacks, has a higher message delivery rate and lower transmission latency than other routers.

Decentralized Edge Intelligence-Driven Network Resource Orchestration Mechanism

With the development of artificial intelligence of things (AIoT), multi-access edge computing (MEC) becomes a key enabler to migrate cloud services to edge clients. In comparison to traditional cloud computing techniques, MEC is characterized with low transmission latency, good flexibility, adaptability and robustness. Nevertheless, traditional resource allocation methods are difficult to meet the requirements of achieving a ubiquitous, pervasive, and intelligent computation offloading strategy in highdynamic network environments. In this paper, we construct an edge intelligence-enabled cloud-edge-client collaborative network structure, and conceive a model-aided multi-agent deep deterministic policy gradient (MA2DDPG) computation offloading framework relying on both centralized training and distributed execution. Simulation results corroborate that our proposed decentralized resource orchestration platform significantly reduces the energy consumption and the transmission latency against state-of-the-art methods. Finally, we highlight open challenges and potential solutions.

Evaluation of GBT-FPGA for timing and fast control in CBM experiment

Timing and Fast Control (TFC) system for the Compressed Baryonic Matter (CBM) experiment is being developed with focus on low and deterministic data transmission latency. This helps to minimize data corruption in the free-streaming Data Acquisition (DAQ) system during occasional data bursts caused by the expected beam intensity fluctuations. Proven in latency-optimized experimental data transport applications, the GBT-FPGA core is expected to positively contribute to the TFC system performance. In this work, the core has been integrated as the primary communication interface and its effect on transmission latency and quality of time distribution has been evaluated.

A novel EO-based optimum random beamforming method in mmWave-NOMA systems with sparse antenna array

Millimeter-wave (mmWave) Non-Orthogonal Multiple Access (NOMA) with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile networks. In this paper, we introduce a cost-effective and energy-efficient mmWave-NOMA system that exploits sparse antenna arrays in the transmitter. Our analysis shows that utilizing low-weight and small-sized sparse antennas in the Base Station (BS) leads to better outage probability performance. We also introduce an optimum low complexity Equilibrium Optimization (EO)-based algorithm to further improve the outage probability. The simulation and analysis results show that the systems equipped with sparse antenna arrays making use of optimum beamforming vectors outperform the conventional systems with uniform linear arrays in terms of outage probability and sum rates.

Guest editorial: Convergence of digital twin and 6G enabled edge intelligence: Theories, algorithms and applications

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and latency-sensitive machine learning based services in future wireless systems. However, with the ever-growing scale of the edge-cloud systems as well as the rapid advances in future 6G technologies, there exists a critical issue that limits the deep penetration of future 6G enabled edge intelligence services, i.e., how to accurately evaluate the performance when leveraging the emerging yet pre-matured technologies, protocols, and algorithms into the existing systems. The recent advanced Digital Twin (DT) has been envisioned to provide a fault-tolerant and low-cost platform for accurately simulating and evaluating the emerging technologies, protocols, and algorithms, without causing any negative influence on the existing systems. The essence of DT aims at enabling a systematic and fully digitalized modeling systems that can produce accurate digital model of the corresponding physical identity in the DT-space. By simulating the digital models in the DT-space, DT can accurately and reliably predict and estimate the dynamics and evolutions of the physical networks during the entire life cycle, thus providing a timely and risk-free methodology for evaluating the performance when adopting the emerging yet pre-matured technologies, protocols, and algorithms for network management. Therefore, DT has been considered as an efficient scheme for addressing the challenging issue to 6G enabled edge intelligence, and thus has attracted lots of interests from both academia and industries. It is observed that DT and edge intelligence can benefit each other, which thus yields a deep convergence of themselves. On the one hand, the increasing network scale, complexity, and security risks of edge intelligence raise more challenges to the security and reliability. DT can accurately simulate and predict the performance of edge intelligence services and thus provide accurate benchmark references for edge services. On the other hand, to enable accurate mapping and real-time synchronization between the physical identifies and their digital models, DT necessitates massive sensing of the targeted physical identifies as well as the consequent data analytics and modeling. Moreover, efficient yet low-latency transmissions of the sensing data and the data analytics (e.g., the inference models) are required. 6G edge intelligence can naturally provide a solution to these requirements. Therefore, this special issue focuses on the convergence of DT and 6G enabled Edge Intelligence, from the perspectives of theories, algorithms, and applications.

Routing and Timeslot Scheduling for SPN Fine-Granularity Slices

The integration of 5G and vertical industries promotes the development of the energy Ethernet while putting forward fine granularity, flexibility, high reliability, and deterministic low-latency service requirements for the smart grid and the ubiquitous power Internet of Things (UPIoT). As the bearer architecture supporting the next-generation optical transmission network, the Slicing Packet Network (SPN) slice granularity decreases from 5 Gbps to 10 Mbps fine granularity and the frame period of 5 Gbps large-granularity slices is short, so the non-deterministic delay caused by timeslot conflicts has a negligible impact on the end-to-end delay, and the timeslot scheduling is unnecessary. However, due to the reduction in timeslot granularity and the change in frame structure in 10 Mbps slices, the scheduling of conflicting timeslots and the complex device computing management problems need to be solved urgently. In this paper, we establish a model of routing embedded timeslot scheduling for the routing of fine-granularity slices and timeslot scheduling problems in SPN-based FlexE interfaces, for which we propose a deterministic timeslot allocation mechanism supporting end-to-end low-latency transmission. According to the timeslot symmetry, the mechanism can reduce the space of feasible solutions through ant colony optimization and unidirectional neighborhood search (ACO-UNS), so as to efficiently solve the scheduling of conflicting timeslots and provide end-to-end delay guarantee for delay-sensitive services. Finally, we make a comparison between the ACO-UNS algorithm and the timeslot random dispatching algorithm (ACO-RD); the results show that, relative to the ACO-RD, the reduction in the proposed ACO-UNS is 98.721% for the end-to-end delay of fine-granularity slices.

QoE-Aware Efficient Content Distribution Scheme For Satellite-Terrestrial Networks

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

Low-Latency Oriented Resource Allocation for MEC-Assisted Adaptive Bitrate Video Streaming

This paper focuses on low-latency transmissions for mobile edge computing (MEC)-assisted adaptive bitrate (ABR) video streaming. With the aim to realize minimum video service latency, an optimization problem is formulated by jointly designing caching, computing and power allocation, subject to diverse video version downloading rate requirements and the limited resources of MEC servers. The formulated optimization problem turns out to be a mixed integer nonlinear programming (MINLP) problem. To solve the problem and satisfy the low-latency service requirement of ABR video streaming, a low-complexity two-step iterative algorithm is proposed by decomposing the MINLP problem into two subproblems, a joint caching and computing subproblem and a power allocation subproblem. Due to the highly coupled nature of the caching and computing, the first subproblem is an integer nonlinear programming (INLP) problem. To solve it efficiently, the INLP problem is equivalently transformed into an integer linear programming (ILP) problem, which is thereafter solved by MATLAB intlinprog function. The second subproblem is a convex problem and is solved by low-complexity power allocation (LCPA) algorithm. Intensive simulation results show that the proposed algorithm converges in a very short time, and can significantly reduce the average latency compared to the state-of-the-art baselines.

Challenges in Implementing Low-Latency Holographic-Type Communication Systems.

Holographic-type communication (HTC) permits new levels of engagement between remote users. It is anticipated that it will give a very immersive experience while enhancing the sense of spatial co-presence. In addition to the newly revealed advantages, however, stringent system requirements are imposed, such as multi-sensory and multi-dimensional data capture and reproduction, ultra-lightweight processing, ultra-low-latency transmission, realistic avatar embodiment conveying gestures and facial expressions, support for an arbitrary number of participants, etc. In this paper, we review the current limitations to the HTC system implementation and systemize the main challenges into a few major groups. Furthermore, we propose a conceptual framework for the realization of an HTC system that will guarantee the desired low-latency transmission, lightweight processing, and ease of scalability, all accompanied with a higher level of realism in human body appearance and dynamics.

Formulating Cost-Effective Data Distribution Strategies Online for Edge Cache Systems

Edge Computing (EC) enables a new kind of caching system in close geographic proximity to end-users by allowing app vendors to cache popular data on edge servers deployed at base stations. This edge cache system can better support latency-sensitive applications. However, transmitting data from the centralized cloud to the edge servers without proper transmission strategies may cost app vendors dearly. Cost-effective data distribution strategies are of particular importance for applications, whose data to be cached at the edge often changes dynamically. In this paper, we study this <i>Online Edge Data Distribution</i> (OEDD) problem, aiming to minimize app vendors’ total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness. We then combine Lyapunov optimization and game theory to propose a novel Latency-Aware Online (LAO) approach for solving this OEDD problem over time in a distributed manner with provable performance guarantees. The evaluation of LAO based on a real-world dataset demonstrates that it can help app vendors formulate cost-effective edge data distribution strategies in an online manner.

A two-level network-on-chip architecture with multicast support

It is essential for implementing processing systems of edge computing, internet of things (IoT) and wireless multimedia sensor networks (WMSN) to use low-power parallel and distributed architectures with high speed and low power consumption. Most of the artificial intelligence and machine learning applications need to be executed on efficient distributed architectures with multicast support. In this paper, TLAM, a high-performance and low-cost NoC architecture is proposed. TLAM stands for Two-Level network-on-chip Architecture with Multicast support and includes a hybrid path/tree based multicast routing algorithm and a specific two-level mesh topology. The routing algorithm of TLAM is basically working according to the Hamiltonian paths. In the proposed architecture, the topology is partitioned and the two-level links provide an efficient infrastructure for low-cost and low-latency transmission of multicast and broadcast messages between partitions. In TLAM, in addition to multicasting routing algorithm, hardware components for handling multicasting packets are designed in order to achieve performance improvements. The goal is to improve the efficiency and performance, especially latency, while handling both unicast and multicast packets. Experimental evaluations including network-level and router-level analysis with different configurations under various traffic patterns were performed. The evaluations in terms of latency, throughput, area and power consumption, indicate that TLAM provides higher performance, especially for dense multicasting and broadcasting, in comparison with the existing architectures.

Context-aware Pseudo-true Video Interpolation at 6G Edge

In the 6G network, lots of edge devices facilitate the low-latency transmission of video. However, with limited processing and storage capabilities, the edge devices cannot afford to reconstruct the vast amount of video data. On the condition of edge computing in the 6G network, this article fuses a self-similarity-based context feature into Frame Rate Up-Conversion (FRUC) to generate the pseudo-true video sequences at high frame rate, and its core is the extraction of the context layer for each video frame. First, we extract the patch centered at each pixel and use the self-similarity descriptor to generate the correlation surface. Then, the expectation or skewness of the correlation surface in statistics is computed to represent its context feature. By attaching an expectation or a skewness to each pixel, the context layer is constructed and added to the video frame as a new channel. According to the context layer, we predict the motion vector field of the absent frame by using the bidirectional context match and finally produce the interpolated frame. From the experimental results, it can be seen that by deploying the proposed FRUC algorithm on edge devices, the output pseudo-true video sequences have satisfying objective and subjective qualities.

INTERLINK: A Digital Twin-Assisted Storage Strategy for Satellite-Terrestrial Networks

Recently, low-orbit satellite networks have gained lots of attention from the society due to their wide coverage, low transmission latency, and storage and computing capacity. Providing seamless connectivity to users in different areas is envisioned as a promising solution, especially in remote areas and for marine communication. However, when jointly used with terrestrial networks composing satellite-terrestrial networks, the satellite moving speed is much faster than the ground terminal, which can cause inconsistent service from a single satellite, and therefore lead to frequent satellite handover. Moreover, due to the dynamic and time slot visibility of satellites, the topology of an intersatellite changes frequently, which results in loops during satellite handover, thereby reducing the utilization of links. To address these problems, we propose a digital twin-assisted storage strategy for satellite-terrestrial networks (INTERLINK), which leverages the digital twins (DTs) to map the satellite networks to virtual space for better communication. Specifically, we first propose a satellite storage-oriented handover scheme to minimize the handover frequency by considering the limited access time and capacity constraints of satellites. Then, a multiobjective optimization problem is formulated to obtain the optimal satellite by genetic algorithm. Finally, considering the timing visibility of the satellite links, a digital twin-assisted intersatellite routing scheme is introduced to improve the quality of data delivery between satellites. Simulation results demonstrate that the proposed INTERLINK can reduce both handover times and average propagation delay compared with its counterparts. Meanwhile, benefitting from integrated DT, both the quality of data delivery and the delay of intersatellite links are considerably improved.

ELIoT: enhancing LiFi for next-generation Internet of things

Communication for the Internet of things (IoT) currently is predominantly narrowband and cannot always guarantee low latency and high reliability. Future IoT applications such as flexible manufacturing, augmented reality and self-driving vehicles rely on sophisticated real-time processing in the cloud to which mobile IoT devices are connected. High-capacity links that meet the requirements of the upcoming 6G systems cannot easily be provided by the current radio-based communication infrastructure. Light communication, which is also denoted as LiFi, offers huge amounts of spectrum, extra security and low-latency transmission free of interference even in dense reuse settings. We present the current state-of-the-art of LiFi systems and introduce new features needed for future IoT applications. We discuss results from a distributed multiple-input multiple-output topology with a fronthaul using plastic optical fibre. We evaluate seamless mobility between the light access points and also handovers to 5G, besides low-power transmission and integrated positioning. Future LiFi development, implementation and efforts towards standardization are addressed in the EU ELIoT project which is presented here.

Resource allocation for D2D-assisted haptic communications

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments, e.g., teleoperation and autonomous driving. Considering the strict transmission requirements on reliability and latency, Device-to-Device (D2D) communications is introduced to assist haptic communications. In particular, the teleoperators with poor channel quality are assisted by auxiliaries, and each auxiliary and its corresponding teleoperator constitute a D2D pair. However, the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation, especially facing the sporadic packet arrivals. First, the contention-based access scheme is applied to achieve low-latency transmission, where the resource scheduling latency is omitted and users can directly access available resources. In this context, we derive the reliability index of D2D pairs under the contention-based access scheme, i.e., closed-loop packet error probability. Then, the reliability performance is guaranteed by bidirectional power control, which aims to minimize the sum packet error probability of all D2D pairs. Potential game theory is introduced to solve the problem with low complexity. Accordingly, a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium. Experimental results demonstrate the superiority of the proposed learning algorithm.