24 research outputs found
Spectrum Sensing for Cognitive Radio Systems Through Primary User Activity Prediction
Traditional spectrum sensing techniques such as energy detection, for instance, can sense the spectrum only when the cognitive radio (CR) is is not in operation. This constraint is relaxed recently by some blind source separation techniques in which the CR can operate during spectrum sensing. The proposed method in this paper uses the fact that the primary spectrum usage is correlated across time and follows a predictable behavior. More precisely, we propose a new spectrum sensing method that can be trained over time to predict the primary user's activity and sense the spectrum even while the CR user is in operation. Performance achieved by the proposed method is compared to classical spectrum sensing methods. Simulation results provided in terms of receiver operating characteristic curves indicate that in addition to the interesting feature that the CR can transmit during spectrum sensing, the proposed method outperforms conventional spectrum sensing techniques
Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks
For improving the efficiency and the reliability of the opportunistic routing
algorithm, in this paper, we propose the cross-layer and reliable opportunistic
routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the
improved efficiency fuzzy logic and humoral regulation inspired topology
control into the opportunistic routing algorithm. In CBRT, the inputs of the
fuzzy logic system are the relative variance (rv) of the metrics rather than
the values of the metrics, which reduces the number of fuzzy rules
dramatically. Moreover, the number of fuzzy rules does not increase when the
number of inputs increases. For reducing the control cost, in CBRT, the node
degree in the candidate relays set is a range rather than a constant number.
The nodes are divided into different categories based on their node degree in
the candidate relays set. The nodes adjust their transmission range based on
which categories that they belong to. Additionally, for investigating the
effection of the node mobility on routing performance, we propose a link
lifetime prediction algorithm which takes both the moving speed and moving
direction into account. In CBRT, the source node determines the relaying
priorities of the relaying nodes based on their utilities. The relaying node
which the utility is large will have high priority to relay the data packet. By
these innovations, the network performance in CBRT is much better than that in
ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201
Mixed Power Control Strategies for Cognitive Radio Networks under SINR and Interference Temperature Constraints
Without consideration of the minimum signal-to-interference-plus-noise ratio (SINR) and frequent information exchange, traditional power control algorithms can not always satisfy SINR requirements of secondary users (SUs) and primary users (PUs) in cognitive radio networks. In this paper, a distributed power control problem for maximizing total throughput of SUs is studied subject to the SINR constraints of SUs and the interference constraints of PUs. To reduce message exchange among SUs, two improved methods are obtained by dual decomposition approaches. For a large-scale network, an average interference constraint is presented at the cost of performance degradation. For a small-scale network, a weighted interference constraint with fairness consideration is proposed to obtain good performance. Simulation results demonstrate that the proposed algorithm is superior to ADCPC and TPCG algorithms
From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
Strategies to acquire white space information is the single most significant functionality in cognitive
radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The
evolution trends are spectrum sensing, prediction algorithm and recently, geo‐location database
technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of
a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not
materialized as a result of numerous technical challenges ranging from hardware imperfections to RF
signal impairments. To convey the evolutionary trends in the development of white space information,
we present a survey of the contemporary advancements in PU detection with emphasis on the practical
deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo‐location database
is the most reliable technique to acquire TVWS information although, it is financially driven. Finally,
using financially driven database model, this study compared the data‐rate and spectral efficiency of FCC
and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV
channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an allinclusive
TVWS information acquisition model as the future research direction for TVWS information
acquisition techniques
A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network
Artificial Neural Network (ANN) is one of the popular techniques used in stock market price prediction. ANN is able to learn from data pattern and continuously improves the result without prior information about the model. The two popular variants of ANN architecture widely used are Feedforward Neural Network (FFNN) and Recurrent Neural Network (RNN). The literature shows that the performance of these two ANN variants is studied dependent. Hence, this paper aims to compare the performance of FFNN and RNN in predicting the closing price of CIMB stock which is traded on the Kuala Lumpur Stock Exchange (KLSE). This paper describes the design of FFNN and RNN and discusses the performances of both ANNs
The SS-SCR Scheme for dynamic spectrum access
We integrate the two models of Cognitive Radio (CR), namely, the conventional Sense-and-Scavenge (SS) Model and Symbiotic Cooperative Relaying (SCR). The resultant scheme, called SS-SCR, improves the efficiency of spectrum usage and reliability of the transmission links. SS-SCR is enabled by a suitable cross-layer optimization problem in a multihop multichannel CR network. Its performance is compared for different PU activity patterns with those schemes which consider SS and SCR separately and perform disjoint resource allocation. Simulation results depict the effectiveness of the proposed SS-SCR scheme. We also indicate the usefulness of cloud computing for a practical deployment of the scheme
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
Sensing or Transmission: Causal Cognitive Radio Strategies with Censorship
This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received signal-to-noise ratios (SNRs). The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB. \ua9 2002-2012 IEEE