75,252 research outputs found
Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation
Context : Software effort estimation is one of the most important activities in the software development process. Unfortunately, estimates are often substantially wrong. Numerous estimation methods have been proposed including Case-based Reasoning (CBR). In order to improve CBR estimation accuracy, many researchers have proposed feature weighting techniques (FWT). Objective: Our purpose is to systematically review the empirical evidence to determine whether FWT leads to improved predictions. In addition we evaluate these techniques from the perspectives of (i) approach (ii) strengths and weaknesses (iii) performance and (iv) experimental evaluation approach including the data sets used. Method: We conducted a systematic literature review of published, refereed primary studies on FWT (2000-2014). Results: We identified 19 relevant primary studies. These reported a range of different techniques. 17 out of 19 make benchmark comparisons with standard CBR and 16 out of 17 studies report improved accuracy. Using a one-sample sign test this positive impact is significant (p = 0:0003). Conclusion: The actionable conclusion from this study is that our review of all relevant empirical evidence supports the use of FWTs and we recommend that researchers and practitioners give serious consideration to their adoption
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
Synchronization in wireless communications
The last decade has witnessed an immense increase of wireless communications services in order to keep pace with the ever increasing demand for higher data rates combined with higher mobility. To satisfy this demand for higher data rates, the throughput over the existing transmission media had to be increased. Several techniques were proposed to boost up the data rate: multicarrier systems to combat selective fading, ultra wide band (UWB) communications systems to share the spectrum with other users, MIMO transmissions to increase the capacity of wireless links, iteratively decodable codes (e.g., turbo codes and LDPC codes) to improve the quality of the link, cognitive radios, and so forth
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
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