1,304 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A Scalable Hybrid MAC Protocol for Massive M2M Networks
In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC)
protocol is crucial to enable numerous machine-type devices to concurrently
access the channel. Most literatures focus on developing simplex (reservation
or contention based)MAC protocols which cannot provide a scalable solution for
M2M networks with large number of devices. In this paper, a frame-based Hybrid
MAC scheme, which consists of a contention period and a transmission period, is
proposed for M2M networks. In the proposed scheme, the devices firstly contend
the transmission opportunities during the contention period, only the
successful devices will be assigned a time slot for transmission during the
transmission period. To balance the tradeoff between the contention and
transmission period in each frame, an optimization problem is formulated to
maximize the system throughput by finding the optimal contending probability
during contention period and optimal number of devices that can transmit during
transmission period. A practical hybrid MAC protocol is designed to implement
the proposed scheme. The analytical and simulation results demonstrate the
effectiveness of the proposed Hybrid MAC protocol
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Probabilistic Rateless Multiple Access for Machine-to-Machine Communication
Future machine to machine (M2M) communications need to support a massive
number of devices communicating with each other with little or no human
intervention. Random access techniques were originally proposed to enable M2M
multiple access, but suffer from severe congestion and access delay in an M2M
system with a large number of devices. In this paper, we propose a novel
multiple access scheme for M2M communications based on the capacity-approaching
analog fountain code to efficiently minimize the access delay and satisfy the
delay requirement for each device. This is achieved by allowing M2M devices to
transmit at the same time on the same channel in an optimal probabilistic
manner based on their individual delay requirements. Simulation results show
that the proposed scheme achieves a near optimal rate performance and at the
same time guarantees the delay requirements of the devices. We further propose
a simple random access strategy and characterized the required overhead.
Simulation results show the proposed approach significantly outperforms the
existing random access schemes currently used in long term evolution advanced
(LTE-A) standard in terms of the access delay.Comment: Accepted to Publish in IEEE Transactions on Wireless Communication
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