547 research outputs found
Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements
This paper considers one-hop device-to-device (D2D)-assisted wireless caching
networks that cache video files of varying quality levels, with the assumption
that the base station can control the video quality but cache-enabled devices
cannot. Two problems arise in such a caching network: file placement problem
and node association problem. This paper suggests a method to cache videos of
different qualities, and thus of varying file sizes, by maximizing the sum of
video quality measures that users can enjoy. There exists an interesting
trade-off between video quality and video diversity, i.e., the ability to
provision diverse video files. By caching high-quality files, the cache-enabled
devices can provide high-quality video, but cannot cache a variety of files.
Conversely, when the device caches various files, it cannot provide a good
quality for file-requesting users. In addition, when multiple devices cache the
same file but their qualities are different, advanced node association is
required for file delivery. This paper proposes a node association algorithm
that maximizes time-averaged video quality for multiple users under a playback
delay constraint. In this algorithm, we also consider request collision, the
situation where several users request files from the same device at the same
time, and we propose two ways to cope with the collision: scheduling of one
user and non-orthogonal multiple access. Simulation results verify that the
proposed caching method and the node association algorithm work reliably.Comment: 13 pages, 11 figures, accepted for publication in IEEE Journal on
Selected Areas in Communication
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
Cross-Layer Energy Efficient Resource Allocation in PD-NOMA based H-CRANs: Implementation via GPU
In this paper, we propose a cross layer energy efficient resource allocation
and remote radio head (RRH) selection algorithm for heterogeneous traffic in
power domain - non-orthogonal multiple access (PD-NOMA) based heterogeneous
cloud radio access networks (H-CRANs). The main aim is to maximize the EE of
the elastic users subject to the average delay constraint of the streaming
users and the constraints, RRH selection, subcarrier, transmit power and
successive interference cancellation. The considered optimization problem is
non-convex, NP-hard and intractable. To solve this problem, we transform the
fractional objective function into a subtractive form. Then, we utilize
successive convex approximation approach. Moreover, in order to increase the
processing speed, we introduce a framework for accelerating the successive
convex approximation for low complexity with the Lagrangian method on graphics
processing unit. Furthermore, in order to show the optimality gap of the
proposed successive convex approximation approach, we solve the proposed
optimization problem by applying an optimal method based on the monotonic
optimization. Studying different scenarios show that by using both PD-NOMA
technique and H-CRAN, the system energy efficiency is improved
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks
Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin
Scheduling Optimization of Heterogeneous Services by Resolving Conflicts
Fifth generation (5G) new radio introduced flexible numerology to provide the
necessary flexibility for accommodating heterogeneous services. However,
optimizing the scheduling of heterogeneous services with differing delay and
throughput requirements over 5G new radio is a challenging task. In this paper,
we investigate near optimal, low complexity scheduling of radio resources for
ultra-reliable low-latency communications (URLLC) when coexisting with enhanced
mobile broadband (eMBB) services. We demonstrate that maximizing the sum
throughput of eMBB services while servicing URLLC users, is, in the long-term,
equivalent to minimizing the number of URLLC placements in the time-frequency
grid; this result stems from reducing the number of infeasible placements for
eMBB, to which we refer to as "conflicts". To meet this new objective, we
propose and investigate new conflict-aware heuristics; a family of "greedy" and
a lightweight heuristic inspired by bin packing optimization, all of near
optimal performance. Moreover, having shed light on the impact of conflict in
layer-2 scheduling, non-orthogonal multiple access (NOMA) emerges as a
competitive approach for conflict resolution, in addition to the well
established increased spectral efficiency with respect to OMA. The superior
performance of NOMA, thanks to alleviating conflicts,is showcased by extensive
numerical results
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed
through smart devices connected to the Internet. In the wake of disruptive IoT
with a huge amount and variety of data, Machine Learning (ML) and Deep Learning
(DL) mechanisms will play a pivotal role to bring intelligence to the IoT
networks. Among other aspects, ML and DL can play an essential role in
addressing the challenges of resource management in large-scale IoT networks.
In this article, we conduct a systematic and in-depth survey of the ML- and
DL-based resource management mechanisms in cellular wireless and IoT networks.
We start with the challenges of resource management in cellular IoT and
low-power IoT networks, review the traditional resource management mechanisms
for IoT networks, and motivate the use of ML and DL techniques for resource
management in these networks. Then, we provide a comprehensive survey of the
existing ML- and DL-based resource allocation techniques in wireless IoT
networks and also techniques specifically designed for HetNets, MIMO and D2D
communications, and NOMA networks. To this end, we also identify the future
research directions in using ML and DL for resource allocation and management
in IoT networks.Comment: 21 pages, 3 figure
Intelligent Scheduling and Power Control for Multimedia Transmission in 5G CoMP Systems: A Dynamic Bargaining Game
Intelligent terminals support a large number of multimedia, such as picture,
audio, video, and so on. The coexistence of various multimedia makes it
necessary to provide service for different requests. In this work, we consider
interference-aware coordinated multi-point (CoMP) to mitigate inter-cell
interference and improve total throughput in the fifth-generation (5G) mobile
networks. To select the scheduled edge users, cluster the cooperative base
stations (BSs), and determine the transmitting power, a novel dynamic
bargaining approach is proposed. Based on affinity propagation, we first select
the users to be scheduled and the cooperative BSs serving them respectively.
Then, based on the Nash bargaining solution (NBS), we develop a power control
scheme considering the transmission delay, which guarantees a generalized
proportional fairness among users. Simulation results demonstrate the
superiority of the user-centric scheduling and power control methods in 5G CoMP
systems.Comment: 11 pages, 14 figures, This paper is accepted for publication in the
IEEE Journal on Selected Areas in Communications (JSAC) Special Issue on
"Multimedia Economics for Future Networks: Theory Methods , and Application"
on 21 April 201
Max-Min Fairness Based on Cooperative-NOMA Clustering for Ultra-Reliable and Low-Latency Communications
In this paper, the performance of a cooperative relaying technique in a
non-orthogonal multiple access (NOMA) system, briefly named cooperative NOMA
(C-NOMA), is considered in short packet communications with finite blocklength
(FBL) codes. We examine the performance of a decode-and-forward (DF) relaying
along with selection combining (SC) and maximum ratio combining (MRC)
strategies at the receiver. Our goal is user clustering based on C-NOMA to
maximize fair throughput in a DL-NOMA scenario. In each cluster, the user with
a stronger channel (strong user) acts as a relay for the other one (weak user),
and optimal power and blocklength are allocated to achieve max-min throughput.Comment: 11 pages, 6 figures, This paper has been submitted for IEEE systems
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Network-Connected UAV Communications: Potentials and Challenges
This article explores the use of network-connected unmanned aerial vehicle
(UAV) communications as a compelling solution to achieve high-rate information
transmission and support ultra-reliable UAV remote command and control. We
first discuss the use cases of UAVs and the resulting communication
requirements, accompanied with a flexible architecture for network-connected
UAV communications. Then, the signal transmission and interference
characteristics are theoretically analyzed, and subsequently we highlight the
design and optimization considerations, including antenna design,
non-orthogonal multiple access communications, as well as network selection and
association optimization. Finally, case studies are provided to show the
feasibility of network-connected UAV communications
Fast Grant Learning-Based Approach for Machine Type Communications with NOMA
In this paper, we propose a non-orthogonal multiple access (NOMA)-based
communication framework that allows machine type devices (MTDs) to access the
network while avoiding congestion. The proposed technique is a 2-step mechanism
that first employs fast uplink grant to schedule the devices without sending a
request to the base station (BS). Secondly, NOMA pairing is employed in a
distributed manner to reduce signaling overhead. Due to the limited capability
of information gathering at the BS in massive scenarios, learning techniques
are best fit for such problems. Therefore, multi-arm bandit learning is adopted
to schedule the fast grant MTDs. Then, constrained random NOMA pairing is
proposed that assists in decoupling the two main challenges of fast uplink
grant schemes namely, active set prediction and optimal scheduling. Using NOMA,
we were able to significantly reduce the resource wastage due to prediction
errors. Additionally, the results show that the proposed scheme can easily
attain the impractical optimal OMA performance, in terms of the achievable
rewards, at an affordable complexity
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