46 research outputs found
Multiframe coded computation for distributed uplink channel decoding
The latest 5G technology in wireless communication has led to an increasing demand for higher data rates and low latencies. The overall latency of the system in a cloud radio access network is greatly affected by the decoding latency in the uplink channel. Various proposed solutions suggest using network function virtualization (NFV). NFV is the process of decoupling the network functions from hardware appliances. This provides the exibility to implement distributed computing and network coding to effectively reduce the decoding latency and improve the reliability of the system. To ensure the system is cost effective, commercial off the shelf (COTS) devices are used, which are susceptible to random runtimes and server failures. NFV coded computation has shown to provide a significant improvement in straggler mitigation in previous work. This work focuses on reducing the overall decoding time while improving the fault tolerance of the system. The overall latency of the system can be reduced by improving the computation efficiency and processing speed in a distributed communication network. To achieve this, multiframe NFV coded computation is implemented, which exploits the advantage of servers with different runtimes. In multiframe coded computation, each server continues to decode coded frames of the original message until the message is decoded. Individual servers can make up for straggling servers or server failures, increasing the fault tolerance and network recovery time of the system. As a consequence, the overall decoding latency of a message is significantly reduced. This is supported by simulation results, which show the improvement in system performance in comparison to a standard NFV coded system
Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations
The Internet of Things (IoT) promises ubiquitous connectivity of everything
everywhere, which represents the biggest technology trend in the years to come.
It is expected that by 2020 over 25 billion devices will be connected to
cellular networks; far beyond the number of devices in current wireless
networks. Machine-to-Machine (M2M) communications aims at providing the
communication infrastructure for enabling IoT by facilitating the billions of
multi-role devices to communicate with each other and with the underlying data
transport infrastructure without, or with little, human intervention. Providing
this infrastructure will require a dramatic shift from the current protocols
mostly designed for human-to-human (H2H) applications. This article reviews
recent 3GPP solutions for enabling massive cellular IoT and investigates the
random access strategies for M2M communications, which shows that cellular
networks must evolve to handle the new ways in which devices will connect and
communicate with the system. A massive non-orthogonal multiple access (NOMA)
technique is then presented as a promising solution to support a massive number
of IoT devices in cellular networks, where we also identify its practical
challenges and future research directions.Comment: To appear in IEEE Communications Magazin
Coded Computation Against Processing Delays for Virtualized Cloud-Based Channel Decoding
The uplink of a cloud radio access network architecture is studied in which
decoding at the cloud takes place via network function virtualization on
commercial off-the-shelf servers. In order to mitigate the impact of straggling
decoders in this platform, a novel coding strategy is proposed, whereby the
cloud re-encodes the received frames via a linear code before distributing them
to the decoding processors. Transmission of a single frame is considered first,
and upper bounds on the resulting frame unavailability probability as a
function of the decoding latency are derived by assuming a binary symmetric
channel for uplink communications. Then, the analysis is extended to account
for random frame arrival times. In this case, the trade-off between average
decoding latency and the frame error rate is studied for two different queuing
policies, whereby the servers carry out per-frame decoding or continuous
decoding, respectively. Numerical examples demonstrate that the bounds are
useful tools for code design and that coding is instrumental in obtaining a
desirable compromise between decoding latency and reliability.Comment: 11 pages and 12 figures, Submitte
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
Towards reliable communication in LTE-A connected heterogeneous machine to machine network
Machine to machine (M2M) communication is an emerging technology that enables heterogeneous devices to communicate with each other without human intervention and thus forming so-called Internet of Things (IoTs). Wireless cellular networks (WCNs) play a significant role in the successful deployment of M2M communication. Specially the ongoing massive deployment of long term evolution advanced (LTE-A) makes it possible to establish machine type communication (MTC) in most urban and remote areas, and by using LTE-A backhaul network, a seamless network communication is being established between MTC-devices and-applications. However, the extensive network coverage does not ensure a successful implementation of M2M communication in the LTE-A, and therefore there are still some challenges.
Energy efficient reliable transmission is perhaps the most compelling demand for various M2M applications. Among the factors affecting reliability of M2M communication are the high endto-end delay and high bit error rate. The objective of the thesis is to provide reliable M2M communication in LTE-A network. In this aim, to alleviate the signalling congestion on air interface and efficient data aggregation we consider a cluster based architecture where the MTC devices are grouped into number of clusters and traffics are forwarded through some special nodes called cluster heads (CHs) to the base station (BS) using single or multi-hop transmissions. In many deployment scenarios, some machines are allowed to move and change their location in the deployment area with very low mobility. In practice, the performance of data transmission often degrades with the increase of distance between neighboring CHs. CH needs to be reselected in such cases. However, frequent re-selection of CHs results in counter effect on routing and reconfiguration of resource allocation associated with CH-dependent protocols. In addition, the link quality between a CH-CH and CH-BS are very often affected by various dynamic environmental factors such as heat and humidity, obstacles and RF interferences. Since CH aggregates the traffic from all cluster members, failure of the CH means that the full cluster will fail. Many solutions have been proposed to combat with error prone wireless channel such as automatic repeat request (ARQ) and multipath routing. Though the above mentioned techniques improve the communication reliability but intervene the communication efficiency. In the former scheme, the transmitter retransmits the whole packet even though the part of the packet has been received correctly and in the later one, the receiver may receive the same information from multiple paths; thus both techniques are bandwidth and energy inefficient. In addition, with retransmission, overall end to end delay may exceed the maximum allowable delay budget.
Based on the aforementioned observations, we identify CH-to-CH channel is one of the bottlenecks to provide reliable communication in cluster based multihop M2M network and present a full solution to support fountain coded cooperative communications. Our solution covers many aspects from relay selection to cooperative formation to meet the user’s QoS requirements. In the first part of the thesis, we first design a rateless-coded-incremental-relay selection (RCIRS) algorithm based on greedy techniques to guarantee the required data rate with a minimum cost. After that, we develop fountain coded cooperative communication protocols to facilitate the data transmission between two neighbor CHs. In the second part, we propose joint network and fountain coding schemes for reliable communication. Through coupling channel coding and network coding simultaneously in the physical layer, joint network and fountain coding schemes efficiently exploit the redundancy of both codes and effectively combat the detrimental effect of fading conditions in wireless channels. In the proposed scheme, after correctly decoding the information from different sources, a relay node applies network and fountain coding on the received signals and then transmits to the destination in a single transmission. Therefore, the proposed schemes exploit the diversity and coding gain to improve the system performance. In the third part, we focus on the reliable uplink transmission between CHs and BS where CHs transmit to BS directly or with the help of the LTE-A relay nodes (RN). We investigate both type-I and type-II enhanced LTE-A networks and propose a set of joint network and fountain coding schemes to enhance the link robustness.
Finally, the proposed solutions are evaluated through extensive numerical simulations and the numerical results are presented to provide a comparison with the related works found in the literature
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&