489 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
Cooperative Full-Duplex Physical and MAC Layer Design in Asynchronous Cognitive Networks
In asynchronous cognitive networks (CNs), where there is no synchronization between primary users (PUs) and secondary users (SUs), spectrum sensing becomes a challenging task. By combining cooperative spectrum sensing and full-duplex (FD) communications in asynchronous CNs, this paper demonstrates improvements in terms of the average throughput of both PUs and SUs for particular transmission schemes. The average throughputs are derived for SUs and PUs under different FD schemes, levels of residual self-interference, and number of cooperative SUs. In particular, we consider two types of FD schemes, namely, FD transmit-sense-reception (FDr) and FD transmit-sense (FDs). FDr allows SUs to transmit and receive data simultaneously, whereas, in FDs, the SUs continuously sense the channel during the transmission time. This paper shows the respective trade-offs and obtains the optimal scheme based on cooperative FD spectrum sensing. In addition, SUs’ average throughput is analyzed under different primary channel utilization and multichannel sensing schemes. Finally, new FD MAC protocol design is proposed and analyzed for FD cooperative spectrum sensing. We found optimum parameters for our proposed MAC protocol to achieve higher average throughput in certain applications
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 Survey on the Communication Protocols and Security in Cognitive Radio Networks
A cognitive radio (CR) is a radio that can change its transmission parameters based on the perceived availability of the spectrum bands in its operating environment. CRs support dynamic spectrum access and can facilitate a secondary unlicensed user to efficiently utilize the available underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose radios need not be CR-enabled. Most of the active research conducted in the area of CRNs has been so far focused on spectrum sensing, allocation and sharing. There is no comprehensive review paper available on the strategies for medium access control (MAC), routing and transport layer protocols, and the appropriate representative solutions for CRNs. In this paper, we provide an exhaustive analysis of the various techniques/mechanisms that have been proposed in the literature for communication protocols (at the MAC, routing and transport layers), in the context of a CRN, as well as discuss in detail several security attacks that could be launched on CRNs and the countermeasure solutions that have been proposed to avoid or mitigate them. This paper would serve as a good comprehensive review and analysis of the strategies for MAC, routing and transport protocols and security issues for CRNs as well as would lay a strong foundation for someone to further delve onto any particular aspect in greater depth
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