951 research outputs found
When Channel Bonding is Beneficial for Opportunistic Spectrum Access Networks
Transmission over multiple frequency bands combined into one logical channel
speeds up data transfer for wireless networks. On the other hand, the
allocation of multiple channels to a single user decreases the probability of
finding a free logical channel for new connections, which may result in a
network-wide throughput loss. While this relationship has been studied
experimentally, especially in the WLAN configuration, little is known on how to
analytically model such phenomena. With the advent of Opportunistic Spectrum
Access (OSA) networks, it is even more important to understand the
circumstances in which it is beneficial to bond channels occupied by primary
users with dynamic duty cycle patterns. In this paper we propose an analytical
framework which allows the investigation of the average channel throughput at
the medium access control layer for OSA networks with channel bonding enabled.
We show that channel bonding is generally beneficial, though the extent of the
benefits depend on the features of the OSA network, including OSA network size
and the total number of channels available for bonding. In addition, we show
that performance benefits can be realized by adaptively changing the number of
bonded channels depending on network conditions. Finally, we evaluate channel
bonding considering physical layer constraints, i.e. throughput reduction
compared to the theoretical throughput of a single virtual channel due to a
transmission power limit for any bonding size.Comment: accepted to IEEE Transactions on Wireless Communication
An analysis on decentralized adaptive MAC protocols for Cognitive Radio networks
The scarcity of bandwidth in the radio spectrum has become more vital since the demand for more and more wireless applications has increased. Most of the spectrum bands have been allocated although many studies have shown that these bands are significantly underutilized most of the time. The problem of unavailability of spectrum and inefficiency in its utilization has been smartly addressed by the Cognitive Radio (CR) Technology which is an opportunistic network that senses the environment, observes the network changes, and then using knowledge gained from the prior interaction with the network, makes intelligent decisions by dynamically adapting their transmission characteristics. In this paper some of the decentralized adaptive MAC protocols for CR networks have been critically analyzed and a novel adaptive MAC protocol for CR networks, DNG-MAC which is decentralized and non-global in nature, has been proposed. The results show the DNG-MAC out performs other CR MAC protocols in terms of time and energy efficiency
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Multi-Channel Scheduling with Optimal Spectrum Channel Hole Filling (MCS-OSHF) for Cognitive Radio Wireless Networks
In this study, a contemporary method of scheduling algorithm has been proposed for working on scheduling of varying size data-frames transmission in CR based wireless networks. The objective of the proposed model is to achieve maximum throughput, and also reduction of loss of dataframes in the transmission. Some of the key elements that are considered in the development of the model are optimal bandwidth and idle channel availability. Using the three level hierarchical approach, the scheduling strategy is constructed. The optimal idle channel allocation, allocation with considerable transmission intervals allocation and optimal multiple channels models are considered at respective levels in the hierarchy in the proposed algorithm. The proposed model while tested under simulated environment in comparison to the other two bench marking models, the outcome depicts that the process is more efficient and supports in improving the overall process of scheduling of data-frames as per the desired objectives of the model
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
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