In this paper, we propose a novel censor-based cooperative spectrum sensing strategy, called adaptive energy-efficient sensing (AES), in which both sequential sensing and censoring report mechanism are employed, aiming to reduce the sensing energy consumption of secondary user relays (SRs). In AES, an anchor secondary user (SU) requires cooperative sensing only when it does not detect the presence of PU by itself, and the cooperative SR adopts decision censoring report only if the sensing result differs from its previous one. We derive the generalized-form expressions false alarm and detection probabilities over Rayleigh fading channels for AES. The sensing energy consumption is also analyzed. Then, we study sensing energy overhead minimization problem and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing energy overhead. Finally, numerical results show that the proposed strategy can remarkably reduce the sensing energy consumption while only slightly degrading the detection performance compared with traditional scheme.

In this paper, we propose a modeling approach for wireless sensor networks (WSNs) that is based on non-volatile two-dimensional cellular automata (CA) and analyze the space-time dynamics of a WSN based on the proposed model. We introduce the fourth circuit element with memory function — memristor into the cells of CA to model a non-volatile CA and employ the non-volatile CA in modeling a WSN. A state transition method is designed to implement the synchronous updates of the states between the central sensor nodes and its neighbors which might behave asynchronously in sending messages to the central one. Therefore, the energy consumption in sensor nodes can be reduced by lessening the amount of exchanged information. Simulations demonstrate that the energy consumption of a WSN can be reduced greatly based on the proposed model and the lifetime of the whole network can be increased.

This paper focuses on power control in relay-enhanced multicell networks with universal frequency reuse for maximizing the overall system throughput, subject to interference and noise impairments, and individual power constraints at both BSs and RSs. With a high signal-to-interference-plus-noise ratio (SINR) approximation, an energy efficiency based power allocation algorithm is proposed to achieve the maximum sum throughput with the least power consumption. Moreover, an iterative quasi-distributed power allocation algorithm is also presented, which is suitable for any SINR regime. Numerical results indicate that the proposed algorithms approach the optimal power allocation and the system performance can be significantly improved in terms of network throughput and energy efficiency.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

Pseudorandom sequences with low autocorrelation magnitude play important roles in various environments. Let N be a prime with N=Mf+1, where M and f are positive integers. A new method to construct M-sequences of period 4N is given. We show that these new sequences have low autocorrelation magnitude.