218 research outputs found
Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks
The problem of distributed rate maximization in multi-channel ALOHA networks
is considered. First, we study the problem of constrained distributed rate
maximization, where user rates are subject to total transmission probability
constraints. We propose a best-response algorithm, where each user updates its
strategy to increase its rate according to the channel state information and
the current channel utilization. We prove the convergence of the algorithm to a
Nash equilibrium in both homogeneous and heterogeneous networks using the
theory of potential games. The performance of the best-response dynamic is
analyzed and compared to a simple transmission scheme, where users transmit
over the channel with the highest collision-free utility. Then, we consider the
case where users are not restricted by transmission probability constraints.
Distributed rate maximization under uncertainty is considered to achieve both
efficiency and fairness among users. We propose a distributed scheme where
users adjust their transmission probability to maximize their rates according
to the current network state, while maintaining the desired load on the
channels. We show that our approach plays an important role in achieving the
Nash bargaining solution among users. Sequential and parallel algorithms are
proposed to achieve the target solution in a distributed manner. The
efficiencies of the algorithms are demonstrated through both theoretical and
simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM
Transactions on Networking, part of this work was presented at IEEE CAMSAP
201
Channel assembling and resource allocation in multichannel spectrum sharing wireless networks
Submitted in fulfilment of the academic requirements for the degree of
Doctor of Philosophy (Ph.D.) in Engineering, in the School of Electrical and
Information Engineering, Faculty of Engineering and the Built Environment,
at the University of the Witwatersrand, Johannesburg, South Africa, 2017The continuous evolution of wireless communications technologies has increasingly imposed a
burden on the use of radio spectrum. Due to the proliferation of new wireless networks applications
and services, the radio spectrum is getting saturated and becoming a limited resource. To a large
extent, spectrum scarcity may be a result of deficient spectrum allocation and management policies,
rather than of the physical shortage of radio frequencies. The conventional static spectrum
allocation has been found to be ineffective, leading to overcrowding and inefficient use. Cognitive
radio (CR) has therefore emerged as an enabling technology that facilitates dynamic spectrum
access (DSA), with a great potential to address the issue of spectrum scarcity and inefficient use.
However, provisioning of reliable and robust communication with seamless operation in cognitive
radio networks (CRNs) is a challenging task. The underlying challenges include development of
non-intrusive dynamic resource allocation (DRA) and optimization techniques.
The main focus of this thesis is development of adaptive channel assembling (ChA) and DRA
schemes, with the aim to maximize performance of secondary user (SU) nodes in CRNs, without
degrading performance of primary user (PU) nodes in a primary network (PN). The key objectives
are therefore four-fold. Firstly, to optimize ChA and DRA schemes in overlay CRNs. Secondly, to
develop analytical models for quantifying performance of ChA schemes over fading channels in
overlay CRNs. Thirdly, to extend the overlay ChA schemes into hybrid overlay and underlay
architectures, subject to power control and interference mitigation; and finally, to extend the
adaptive ChA and DRA schemes for multiuser multichannel access CRNs.
Performance analysis and evaluation of the developed ChA and DRA is presented, mainly through
extensive simulations and analytical models. Further, the cross validation has been performed
between simulations and analytical results to confirm the accuracy and preciseness of the novel
analytical models developed in this thesis. In general, the presented results demonstrate improved
performance of SU nodes in terms of capacity, collision probability, outage probability and forced
termination probability when employing the adaptive ChA and DRA in CRNs.CK201
An Online Approach to Dynamic Channel Access and Transmission Scheduling
Making judicious channel access and transmission scheduling decisions is
essential for improving performance as well as energy and spectral efficiency
in multichannel wireless systems. This problem has been a subject of extensive
study in the past decade, and the resulting dynamic and opportunistic channel
access schemes can bring potentially significant improvement over traditional
schemes. However, a common and severe limitation of these dynamic schemes is
that they almost always require some form of a priori knowledge of the channel
statistics. A natural remedy is a learning framework, which has also been
extensively studied in the same context, but a typical learning algorithm in
this literature seeks only the best static policy, with performance measured by
weak regret, rather than learning a good dynamic channel access policy. There
is thus a clear disconnect between what an optimal channel access policy can
achieve with known channel statistics that actively exploits temporal, spatial
and spectral diversity, and what a typical existing learning algorithm aims
for, which is the static use of a single channel devoid of diversity gain. In
this paper we bridge this gap by designing learning algorithms that track known
optimal or sub-optimal dynamic channel access and transmission scheduling
policies, thereby yielding performance measured by a form of strong regret, the
accumulated difference between the reward returned by an optimal solution when
a priori information is available and that by our online algorithm. We do so in
the context of two specific algorithms that appeared in [1] and [2],
respectively, the former for a multiuser single-channel setting and the latter
for a single-user multichannel setting. In both cases we show that our
algorithms achieve sub-linear regret uniform in time and outperforms the
standard weak-regret learning algorithms.Comment: 10 pages, to appear in MobiHoc 201
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
Analysis and Optimization of Random Sensing Order in Cognitive Radio Networks
Developing an efficient spectrum access policy enables cognitive radios to
dramatically increase spectrum utilization while ensuring predetermined quality
of service levels for primary users. In this paper, modeling, performance
analysis, and optimization of a distributed secondary network with random
sensing order policy are studied. Specifically, the secondary users create a
random order of available channels upon primary users return, and then find
optimal transmission and handoff opportunities in a distributed manner. By a
Markov chain analysis, the average throughputs of the secondary users and
average interference level among the secondary and primary users are
investigated. A maximization of the secondary network performance in terms of
the throughput while keeping under control the average interference is
proposed. It is shown that despite of traditional view, non-zero false alarm in
the channel sensing can increase channel utilization, especially in a dense
secondary network where the contention is too high. Then, two simple and
practical adaptive algorithms are established to optimize the network. The
second algorithm follows the variations of the wireless channels in
non-stationary conditions and outperforms even static brute force optimization,
while demanding few computations. The convergence of the distributed algorithms
are theoretically investigated based on the analytical performance indicators
established by the Markov chain analysis. Finally, numerical results validate
the analytical derivations and demonstrate the efficiency of the proposed
schemes. It is concluded that fully distributed sensing order algorithms can
lead to substantial performance improvements in cognitive radio networks
without the need of centralized management or message passing among the users.Comment: 16 pages, 12 figures, 7 tables, accepted in Journal of Selected Areas
in Communications (J-SAC) CR series and will be published in Apr'1
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