Enhanced Pilot-Based Spectrum Sensing Algorithm

In this paper, we develop an enhanced pilot-based spectrum sensing algorithm for cognitive radio. Unlike conventional pilot-based detectors which merely detect the presence of pilot signals, the proposed detector also utilizes the presence of the signal that carries the actual information. We analytically compare the performance of the proposed detector with the conventional one, and we show that the detection performance is significantly improved.Comment: 4 pages, 2 figures; published in Proc. IEEE Biennial Symps. on Commun. (QBSC'14), June 201

Stochastic analysis of neural network modeling and identification of nonlinear memoryless MIMO systems

Neural network (NN) approaches have been widely applied for modeling and identification of nonlinear multiple-input multiple-output (MIMO) systems. This paper proposes a stochastic analysis of a class of these NN algorithms. The class of MIMO systems considered in this paper is composed of a set of single-input nonlinearities followed by a linear combiner. The NN model consists of a set of single-input memoryless NN blocks followed by a linear combiner. A gradient descent algorithm is used for the learning process. Here we give analytical expressions for the mean squared error (MSE), explore the stationary points of the algorithm, evaluate the misadjustment error due to weight fluctuations, and derive recursions for the mean weight transient behavior during the learning process. The paper shows that in the case of independent inputs, the adaptive linear combiner identifies the linear combining matrix of the MIMO system (to within a scaling diagonal matrix) and that each NN block identifies the corresponding unknown nonlinearity to within a scale factor. The paper also investigates the particular case of linear identification of the nonlinear MIMO system. It is shown in this case that, for independent inputs, the adaptive linear combiner identifies a scaled version of the unknown linear combining matrix. The paper is supported with computer simulations which confirm the theoretical results

Neural network modelling of nonlinear channels with memory

Le papier propose une approche basée sur les réseaux de neurones pour modéliser les canaux non linéaires avec mémoire. Ce papier présente uniquement les algorithmes et leur comportement pendant l'apprentissage. Plusieurs applications peuvent être trouvées dans [5]

A consensus-based protocol for spectrum sharing fairness in cognitive radio ad hoc and sensor networks

Spectrum sharing fairness is an important topic in cognitive radio ad hoc networks (CRAHNs) and cognitive radio sensor networks (CRSNs). Consensus-based protocols can provide light-weight and efficient solutions for CRAHNs and CRSNs but the theoretical ground needs to be investigated for spectrum sharing fairness. In this paper, we investigate the convergence condition when applying a consensus-based protocol to spectrum sharing while ensuring spectrum sharing fairness. Based on the local observation and local control scheme using spectrum-related information, an individual cognitive node can effectively perform the spectrum sharing. Then we propose a consensus-based protocol for spectrum sharing. Supported with computer simulation results, we show the effectiveness of using the proposed consensus-based protocol to solve the spectrum sharing problems in CRAHNs and CRSNs

VNF placement optimization at the edge and cloud

Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions t

Precoding-Aided Spatial Modulation for the Wiretap Channel with Relay Selection and Cooperative Jamming

We propose in this paper a physical-layer security (PLS) scheme for dual-hop cooperative networks in an effort to enhance the communications secrecy. The underlying model comprises a transmitting node (Alice), a legitimate node (Bob), and an eavesdropper (Eve). It is assumed that there is no direct link between Alice and Bob, and the communication between them is done through trusted relays over two phases. In the first phase, precoding-aided spatial modulation (PSM) is employed, owing to its low interception probability, while simultaneously transmitting a jamming signal from Bob. In the second phase, the selected relay detects and transmits the intended signal, whereas the remaining relays transmit the jamming signal received from Bob. We analyze the performance of the proposed scheme in terms of the ergodic secrecy capacity (ESC), the secrecy outage probability (SOP), and the bit error rate (BER) at Bob and Eve. We obtain closed-form expressions for the ESC and SOP and we derive very tight upper-bounds for the BER. We also optimize the performance with respect to the power allocation among the participating relays in the second phase. We provide examples with numerical and simulation results through which we demonstrate the effectiveness of the proposed scheme

Simulators and experimental testbeds design and development for wireless networks

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