3,741 research outputs found
Throughput Optimal Multi-user Scheduling via Hierarchical Modulation
We investigate the network stability problem when two users are scheduled
simultaneously. The key idea is to simultaneously transmit to more than one
users experiencing different channel conditions by employing hierarchical
modulation. For two-user scheduling problem, we develop a throughput-optimal
algorithm which can stabilize the network whenever this is possible. In
addition, we analytically prove that the proposed algorithm achieves larger
achievable rate region compared to the conventional Max-Weight algorithm which
employs uniform modulation and transmits a single user. We demonstrate the
efficacy of the algorithm on a realistic simulation environment using the
parameters of High Data Rate protocol in a Code Division Multiple Access
system. Simulation results show that with the proposed algorithm, the network
can carry higher user traffic with lower delays.Comment: 4 pages, 2 figures, submitte
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
Partially-Distributed Resource Allocation in Small-Cell Networks
We propose a four-stage hierarchical resource allocation scheme for the
downlink of a large-scale small-cell network in the context of orthogonal
frequency-division multiple access (OFDMA). Since interference limits the
capabilities of such networks, resource allocation and interference management
are crucial. However, obtaining the globally optimum resource allocation is
exponentially complex and mathematically intractable. Here, we develop a
partially decentralized algorithm to obtain an effective solution. The three
major advantages of our work are: 1) as opposed to a fixed resource allocation,
we consider load demand at each access point (AP) when allocating spectrum; 2)
to prevent overloaded APs, our scheme is dynamic in the sense that as the users
move from one AP to the other, so do the allocated resources, if necessary, and
such considerations generally result in huge computational complexity, which
brings us to the third advantage: 3) we tackle complexity by introducing a
hierarchical scheme comprising four phases: user association, load estimation,
interference management via graph coloring, and scheduling. We provide
mathematical analysis for the first three steps modeling the user and AP
locations as Poisson point processes. Finally, we provide results of numerical
simulations to illustrate the efficacy of our scheme.Comment: Accepted on May 15, 2014 for publication in the IEEE Transactions on
Wireless Communication
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Dynamic Time-domain Duplexing for Self-backhauled Millimeter Wave Cellular Networks
Millimeter wave (mmW) bands between 30 and 300 GHz have attracted
considerable attention for next-generation cellular networks due to vast
quantities of available spectrum and the possibility of very high-dimensional
antenna ar-rays. However, a key issue in these systems is range: mmW signals
are extremely vulnerable to shadowing and poor high-frequency propagation.
Multi-hop relaying is therefore a natural technology for such systems to
improve cell range and cell edge rates without the addition of wired access
points. This paper studies the problem of scheduling for a simple
infrastructure cellular relay system where communication between wired base
stations and User Equipment follow a hierarchical tree structure through fixed
relay nodes. Such a systems builds naturally on existing cellular mmW backhaul
by adding mmW in the access links. A key feature of the proposed system is that
TDD duplexing selections can be made on a link-by-link basis due to directional
isolation from other links. We devise an efficient, greedy algorithm for
centralized scheduling that maximizes network utility by jointly optimizing the
duplexing schedule and resources allocation for dense, relay-enhanced OFDMA/TDD
mmW networks. The proposed algorithm can dynamically adapt to loading, channel
conditions and traffic demands. Significant throughput gains and improved
resource utilization offered by our algorithm over the static,
globally-synchronized TDD patterns are demonstrated through simulations based
on empirically-derived channel models at 28 GHz.Comment: IEEE Workshop on Next Generation Backhaul/Fronthaul Networks -
BackNets 201
Performance analysis of carrier aggregation for various mobile network implementations scenario based on spectrum allocated
Carrier Aggregation (CA) is one of the Long Term Evolution Advanced (LTE-A)
features that allow mobile network operators (MNO) to combine multiple
component carriers (CCs) across the available spectrum to create a wider
bandwidth channel for increasing the network data throughput and overall
capacity. CA has a potential to enhance data rates and network performance in
the downlink, uplink, or both, and it can support aggregation of frequency
division duplexing (FDD) as well as time division duplexing (TDD). The
technique enables the MNO to exploit fragmented spectrum allocations and can be
utilized to aggregate licensed and unlicensed carrier spectrum as well. This
paper analyzes the performance gains and complexity level that arises from the
aggregation of three inter-band component carriers (3CC) as compared to the
aggregation of 2CC using a Vienna LTE System Level simulator. The results show
a considerable growth in the average cell throughput when 3CC aggregations are
implemented over the 2CC aggregation, at the expense of reduction in the
fairness index. The reduction in the fairness index implies that, the scheduler
has an increased task in resource allocations due to the added component
carrier. Compensating for such decrease in the fairness index could result into
scheduler design complexity. The proposed scheme can be adopted in combining
various component carriers, to increase the bandwidth and hence the data rates.Comment: 13 page
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