2,053 research outputs found
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
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
Data Transmission with Reduced Delay for Distributed Acoustic Sensors
This paper proposes a channel access control scheme fit to dense acoustic
sensor nodes in a sensor network. In the considered scenario, multiple acoustic
sensor nodes within communication range of a cluster head are grouped into
clusters. Acoustic sensor nodes in a cluster detect acoustic signals and
convert them into electric signals (packets). Detection by acoustic sensors can
be executed periodically or randomly and random detection by acoustic sensors
is event driven. As a result, each acoustic sensor generates their packets
(50bytes each) periodically or randomly over short time intervals
(400ms~4seconds) and transmits directly to a cluster head (coordinator node).
Our approach proposes to use a slotted carrier sense multiple access. All
acoustic sensor nodes in a cluster are allocated to time slots and the number
of allocated sensor nodes to each time slot is uniform. All sensor nodes
allocated to a time slot listen for packet transmission from the beginning of
the time slot for a duration proportional to their priority. The first node
that detect the channel to be free for its whole window is allowed to transmit.
The order of packet transmissions with the acoustic sensor nodes in the time
slot is autonomously adjusted according to the history of packet transmissions
in the time slot. In simulations, performances of the proposed scheme are
demonstrated by the comparisons with other low rate wireless channel access
schemes.Comment: Accepted to IJDSN, final preprinted versio
Cognition-inspired 5G cellular networks: a review and the road ahead
Despite the evolution of cellular networks, spectrum scarcity and the lack of intelligent and autonomous capabilities remain a cause for concern. These problems have resulted in low network capacity, high signaling overhead, inefficient data forwarding, and low scalability, which are expected to persist as the stumbling blocks to deploy, support and scale next-generation applications, including smart city and virtual reality. Fifth-generation (5G) cellular networking, along with its salient operational characteristics - including the cognitive and cooperative capabilities, network virtualization, and traffic offload - can address these limitations to cater to future scenarios characterized by highly heterogeneous, ultra-dense, and highly variable environments. Cognitive radio (CR) and cognition cycle (CC) are key enabling technologies for 5G. CR enables nodes to explore and use underutilized licensed channels; while CC has been embedded in CR nodes to learn new knowledge and adapt to network dynamics. CR and CC have brought advantages to a cognition-inspired 5G cellular network, including addressing the spectrum scarcity problem, promoting interoperation among heterogeneous entities, and providing intelligence and autonomous capabilities to support 5G core operations, such as smart beamforming. In this paper, we present the attributes of 5G and existing state of the art focusing on how CR and CC have been adopted in 5G to provide spectral efficiency, energy efficiency, improved quality of service and experience, and cost efficiency. This main contribution of this paper is to complement recent work by focusing on the networking aspect of CR and CC applied to 5G due to the urgent need to investigate, as well as to further enhance, CR and CC as core mechanisms to support 5G. This paper is aspired to establish a foundation and to spark new research interest in this topic. Open research opportunities and platform implementation are also presented to stimulate new research initiatives in this exciting area
A Two-Stage Allocation Scheme for Delay-Sensitive Services in Dense Vehicular Networks
Driven by the rapid development of wireless communication system, more and
more vehicular services can be efficiently supported via vehicle-to-everything
(V2X) communications. In order to allocate radio resource with the reasonable
implementation complexity in dense urban intersection, a two-stage allocation
algorithm is proposed in this paper, whose main objective is to minimize delay
and ensure reliability. In particular, as for the first stage, the allocation
policy is based on traffic density information (TDI), which is different from
utilizing channel state information (CSI) and queue state information (QSI) in
the second stage. Moreover, in order to reflect the influence of TDI on delay,
a macroscopic vehicular mobility model is employed in this paper. Simulation
results show that the proposed algorithm can acquire an asymptotically optimal
performance with the acceptable complexity
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