The spectrum sensing duration and the secondary user's achievable throughput trade-off is optimized under outage constraints in a cooperative cognitive radio network over Nakagami fading conditions. The optimum trade-off depends not only on the maximum outage probability allowed by the primary user and the number of cognitive users involved in the sensing but also on the fading operational conditions of the cognitive radio. The parameters of the Nakagami fading distribution can be adjusted to fit different fading environments. The fading parameter (also called shape parameter) of the Nakagami distribution affects the optimum sensing time, which increases when the fading conditions are worse than Rayleigh fading. For the energy detector, the expression for the probability of false alarm is obtained in terms of a new threshold, which is derived considering an outage constraint on the channel-dependent probability of detection. Then, the optimization problem is formulated. Simulation results quantify the effects of different fading parameters on the optimum spectrum sensing duration.

This paper describes the implementation of a Doppler spectrum measurement platform for narrowband frequency-dispersive vehicle-to-vehicle (V2V) channels. The platform is based on a continuous-wave (CW) channel sounding approach widely used for path-loss and large-scale fading measurements, but whose effectiveness to measure the Doppler spectrum of V2V channels is not equally known. This channel sounding method is implemented using general-purpose hardware in a configuration that is easy to replicate and that enables a partial characterization of frequency-dispersive V2V channels at a fraction of the cost of a dedicated channel sounder. The platform was assessed in a series of field experiments that collected empirical data of the instantaneous Doppler spectrum, the mean Doppler shift, the Doppler spread, the path-loss profile, and the large-scale fading distribution of V2V channels under realistic driving conditions. These experiments were conducted in a highway scenario near San Luis Potosí, México, at two different carrier frequencies, one at 760MHz and the other at 2,500MHz. The transmitting and receiving vehicles were moving in the same direction at varying speeds, ranging from 20 to 130km/h and dictated by the unpredictable traffic conditions. The obtained results demonstrate that the presented measurement platform enables the spectral characterization of narrowband V2V channels and the identification of their Doppler signatures in relevant road-safety scenarios, such as those involving overtaking maneuvers and rapid vehicles approaching the transmitter and receiver in the opposite direction.

The 2.4 GHz industrial, scientific and medical (ISM) band is a shared electromagnetic resource where many wireless communications technologies such as wireless local area networks, Bluetooth, ZigBee, cordless phones, amongst others, coexist. Due to the large amount of users of different technologies that might utilize the band at the same time and geographical region, it is reasonable to assume that the band is very busy all the time. In this paper, however, a spectrum measurement campaign is carried out to determine the actual spectrum occupancy of the ISM band in an environment with high density of users. Although many wireless technologies share this frequency band, results show its low spectrum utilization. Indeed, it is shown that the spectral occupation of the band ranges between 7% and 34% of a normal working day, and it is moderate even during busy hours. Therefore, the use of a more sophisticated approach is essential to exploit the unlicensed access to this band, for which cognitive radio is well suited. This is valid despite the fact that, formally speaking, there are no licensed (or primary) users in the ISM band. Nevertheless, ISM users could be treated as pseudo-primary. Thus, cognitive radio techniques can be implemented to improve the spectrum utilization.

This work proposes and describes a deep learning-based approach utilizing recurrent neural networks (RNNs) for beam pattern synthesis considering uniform linear arrays. In this particular case, the deep neural network (DNN) learns from previously optimized radiation patterns as inputs and generates complex excitations as output. Beam patterns are optimized using a genetic algorithm during the training phase in order to reduce sidelobes and achieve high directivity. Idealized and test beam patterns are employed as inputs for the DNN, demonstrating their effectiveness in scenarios with high prediction complexity and closely spaced elements. Additionally, a comparative analysis is conducted among the three DNN architectures. Numerical experiments reveal improvements in performance when using the long short-term memory network (LSTM) compared to fully connected and convolutional neural networks.

Spectrum sensing is a key feature in cognitive radio networks. We propose a locally optimum (LO) detector, which is known to be optimum at low SNR. This is desirable in cognitive radio since the primary user's signal may exhibit a very low power at the cognitive user's transmitter. We focus here on linear modulation in the presence of an unknown phase shift and additive white Gaussian noise. In the case of BPSK modulation, the sufficient statistic of the LO detector is shown to be the sum of the absolute second-order moment (i.e., energy) and second-order moment (pseudo-energy) estimates. For higher size constellation, it is proven that the energy detector is locally optimum. The paper also addresses the issue of noise power uncertainty, to which the energy detector is very sensitive. Although the proposed LO detector is shown to be less sensitive than the energy detector, its performance does deteriorate at high noise power mismatch values. To overcome this problem, we also propose a detector whose probability of false alarm is independent of the noise power. Simulation results show that the proposed detectors in the case of BPSK significantly outperform the energy detector. Further, the complexity of the proposed detectors is only slightly higher than that of the energy detector.

In this work, a comparison of the microwave absorption properties of Opuntia ficus-indica cladodes, Agave atrovirens branches, and Cocos nucifera L. husk samples was performed. The study was carried out by inserting dry and powdered samples of these organic materials transversely and in the middle of a rectangular waveguide, for which scattering parameters S₂₁ and S₁₁ were measured to estimate the absorption coefficient. These measurements were compared to determine the material that behaves the best as a microwave absorber with a view to develop future low-cost and eco-friendly products by reusing agricultural waste. Specifically, Agave atrovirens sample showed the best performance, having an average value of absorption coefficient of 0.4218, while its maximum was 0.5792 at 9.706 GHz within the range from 8.005 to 13 GHz.