Xianjun Yang

Energy-Efficient Distributed Data Storage for Wireless Sensor Networks Based on Compressed Sensing and Network Coding

Recently, distributed data storage (DDS) for Wireless Sensor Networks (WSNs) has attracted great attention, especially in catastrophic scenarios. Since power consumption is one of the most critical factors that affect the lifetime of WSNs, the energy efficiency of DDS in WSNs is investigated in this paper. Based on Compressed Sensing (CS) and network coding theories, we propose a Compressed Network Coding based Distributed data Storage (CNCDS) scheme by exploiting the correlation of sensor readings. The CNCDS scheme achieves high energy efficiency by reducing the total number of transmissions Nttot and receptions Nrtot during the data dissemination process. Theoretical analysis proves that the CNCDS scheme guarantees good CS recovery performance. In order to theoretically verify the efficiency of the CNCDS scheme, the expressions for Nttot and Nrtot are derived based on random geometric graphs (RGG) theory. Furthermore, based on the derived expressions, an adaptive CNCDS scheme is proposed to further reduce Nttot and Nrtot. Simulation results validate that, compared with the conventional ICStorage scheme, the proposed CNCDS scheme reduces Nttot, Nrtot, and the CS recovery mean squared error (MSE) by up to 55%, 74%, and 76% respectively. In addition, compared with the CNCDS scheme, the adaptive CNCDS scheme further reduces Nttot and Nrtot by up to 63% and 32% respectively.

Optimal Energy-Efficient Relay Deployment for the Bidirectional Relay Transmission Schemes

Recently, the energy efficiency of a relay network has become a hot research topic in the wireless communication society. In this paper, we investigate the energy efficiency of three basic bidirectional relay transmission schemes [i.e., the four time-slot (4TS), three time-slot (3TS), and two time-slot (2TS) schemes] from the angle of relay deployment. Since a realistic power consumption model is very important in analyzing energy efficiency, and a power amplifier (PA) consumes up to 70% of the total power, we consider a realistic nonideal PA model. The derived closed-form expressions for the optimal relay deployment and the simulation results reveal the following important conclusions. First, it is possible to achieve the optimal energy efficiency and enlarge the cell coverage simultaneously in bad channel conditions, but it may be very challenging in good channel conditions. Second, under asymmetric traffic conditions, particularly when the downlink rate is larger than the uplink rate, all the aforementioned three schemes have almost the same optimal relay deployment, but the 2TS scheme has the highest energy efficiency when the spectral efficiency is large. Third, the relay node should be deployed closer to the base station with the nonideal PA than that with the ideal PA, and the optimal energy efficiency with the nonideal PA is much higher than that with the ideal PA. Moreover, the impact of small-scale fading depends on the value of path loss. To overcome the small-scale fading, the relay network needs to consume more energy.

Random circulant orthogonal matrix based Analog Compressed Sensing

Analog Compressed Sensing (CS) has attracted considerable research interest in sampling area. One of the promising analog CS technique is the recently proposed Modulated Wideband Converter (MWC). However, MWC has a very high hardware complexity due to its parallel structure. To reduce the hardware complexity of MWC, this paper proposes a novel Random Circulant Orthogonal Matrix based Analog Compressed Sensing (RCOM-ACS) scheme. By circularly shifting the periodic mixing function, the RCOM-ACS scheme reduces the number of physical parallel channels from m to 1 at the cost of longer processing time, where m is in the order of several dozen to several hundred in MWC. It is proved that the m×M measurement matrix of RCOM-ACS scheme satisfies the Restricted Isometry Property (RIP) condition with probability 1−M−O(1) when m = O(rlog2Mlog3r), where M is the length of the periodic mixing function, r denotes the sparsity of the input signal. Furthermore, to make a good tradeoff between processing time and hardware complexity, a short processing time RCOM-ACS scheme is proposed in this paper. Simulation results show that, the proposed schemes outperform MWC in terms of recovery performance.

Multi-antenna compressed wideband spectrum sensing for cognitive radio

This paper proposes a novel wideband spectrum sensing (WSS) scheme, termed multi-antenna compressed wideband spectrum sensing (MCWSS) scheme, which utilizes compressed sensing (CS) to reduce the extremely high sampling rate of wideband signal. Although there are studies on compressed wideband spectrum sensing, they only focus on single antenna signal. Since multi-antenna technology can enhance the detection performance, this paper investigates the multi-antenna scenario. However, existing CS recovery algorithms are designed only for single antenna signal and are not suitable for recovering multi-antenna signals. Therefore, the paper proposes two novel CS recovery algorithms from different angles, namely CRL2 (combining relevance via L2 norm) algorithm and CBS (combining before sampling) algorithm. The CRL2 algorithm jointly recovers the multi-antenna signals and performs better than single antenna scenario. Whereas, CBS algorithm can significantly improves the recovery performance with an additional analog combining operation. Since existing WSS algorithms are too complicated, we devise a novel WSS algorithm, i.e. DA (divided-averaged) algorithm, which has good performance with low complexity. Simulation results show that the MCWSS scheme performs well at low sampling rate.

Anti-noise-folding regularized subspace pursuit recovery algorithm for noisy sparse signals

Denoising recovery algorithms are very important for the development of compressed sensing (CS) theory and its applications. Considering the noise present in both the original sparse signal x and the compressive measurements y, we propose a novel denoising recovery algorithm, named Regularized Subspace Pursuit (RSP). Firstly, by introducing a data pre-processing operation, the proposed algorithm alleviates the noise-folding effect caused by the noise added to x. Then, the indices of the nonzero elements in x are identified by regularizing the chosen columns of the measurement matrix. Afterwards, the chosen indices are updated by retaining only the largest entries in the Minimum Mean Square Error (MMSE) estimated signal. Simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, the proposed RSP algorithm increases the successful recovery rate (and reduces the reconstruction error) by up to 50% and 86% (35% and 65%) in high noise level scenarios and inadequate measurements scenarios, respectively.

Compressed Network Coding for Distributed Storage in Wireless Sensor Networks

Distributed storage plays a very important role in Wireless Sensor Networks (WSNs), especially in catastrophic scenarios. To improve the energy efficiency of distributed storage, this paper proposes a Compressed Network Coding based Distributed Storage (CNCDS) scheme. Exploiting the correlation of sensor readings and utilizing the Compressed Sensing (CS) theory and network coding technology, the proposed CNCDS scheme achieves good energy efficiency by reducing the number of transmissions and receptions. Theoretical analysis proves that, the measurement matrix of CNCDS scheme guarantees good CS recovery performance. Simulation results show that, compared with the conventional ICStorage scheme, the proposed CNCDS scheme reduces the number of transmissions, the number of receptions and the CS recovery mean squared error (MSE) by up to 55%, 74% and 76% respectively. In contrast to the conventional NICStorage scheme, the proposed CNCDS scheme can simultaneously reduce the number of transmissions, receptions and recovery MSE.

Performance bounds of compressed sensing recovery algorithms for sparse noisy signals

Recently, the performance bounds of the compressed sensing (CS) recovery algorithms have been investigated in the noisy setting. However, most of the papers only focus on the noisy measurement model where the signal is noiseless and the noise enters after the CS operation. The noisy signal model where both the signal and the compressed measurements are contaminated by the different noises is not considered. This paper works on the noisy signal model and provides the performance bounds for the following popular recovery algorithms: thresholding and orthogonal matching pursuit (OMP), Dantzig selector (DS) and basis pursuit denoising (BPDN). The performance of the recovery algorithms is quantified as the ℓ2 distance between the reconstructed signal and the true noisy signal. Next, the impacts of the noise are analyzed on the basis of the quantified performance. The analysis results show that the effective way to restrain the impact of the noise is to choose the measurement matrix with low correlation between the columns or the rows. Finally, the theoretical bounds are verified with numerical simulations by calculating the mean-squared-error for the different noise variances. The simulation results show that OMP owns the better performance than the other three recovery algorithms under the noisy signal model.

Interference-constrained adaptive simultaneous spectrum sensing and data transmission scheme for unslotted cognitive radio network

Cognitive radio (CR) is widely recognized as a novel approach to improve the spectrum efficiency. However, there exists one problem needed to be resolved urgently, that is the two conflicting goals in CR network: one is to minimize the interference to primary (licensed) system; the other is to maximize the throughput of secondary (unlicensed) system. Meanwhile, the secondary user (SU) has to monitor the spectrum continuously to avoid the interference to primary user (PU), thus the throughput of the secondary system is affected by how often and how long the spectrum sensing is performed. Aiming to balance the two conflicting goals, this article proposes a novel Interference-Constrained Adaptive Simultaneous spectrum Sensing and data Transmission (ICASST) scheme for unslotted CR network, where SUs are not synchronized with PUs. In the ICASST scheme, taking advantage of the statistic information of PUs activities, the data transmission time is adaptively adjusted to avoid the interference peculiar to unslotted CR network; the operation of spectrum sensing is moved to SU receiver from SU transmitter to increase the data transmission time and hence improve the throughput of SU. Simulation results validate the efficiency of ICASST scheme, which significantly increases the throughput of secondary system and decreases the interference to PU simultaneously.

Analog compressed sensing for multiband signals with non-modulated Slepian basis

Recently, the recovery performance of analog Compressed Sensing (CS) has been significantly improved by representing multiband signals with the modulated and merged Slepian basis (MM-Slepian dictionary), which avoids the frequency leakage effect of the Discrete Fourier Transform (DFT) basis. However, the MM-Slepian dictionary has a very large scale and corresponds to a large-scale measurement matrix, which leads to high recovery computational complexity. This paper resolves the above problem by modulating and band-limiting the multiband signal rather than modulating the Slepian basis. Specifically, instead of using the MM-Slepian dictionary to represent the whole multiband signal, we propose to use the non-modulated Slepian basis to represent the modulated and band-limited version of the multiband signal based on the recently proposed Modulated Wideband Converter (MWC). Furthermore, based on the analytical derivation with the non-modulated Slepian basis, we propose an Interpolation Recovery (IR) algorithm to take full advantage of the Slepian basis, whereas the Direct Recovery (DR) algorithm using the Moore-Penrose pseudo-inverse cannot achieve this. Simulation results verify that, with low recovery computational load, the non-modulated Slepian basis combined with the IR algorithm improves the recovery SNR by up to 35 dB compared with the DFT basis in noise-free environment.

A Multistep Detection Scheme Based on Iteration for Cooperative Spectrum Sensing in Cognitive Radio

In cognitive radio (CR) networks, two main challenges faced by cooperative spectrum sensing are low detection performance and long detection time in low SNR scenario. To the best of our knowledge, little study analyzes these two problems simultaneously and thoroughly. Therefore, this paper proposes a novel spectrum sensing scheme, termed multistep detection (MD) scheme, which aims to resolve the above two problems. For MD scheme which consists of five steps, we focus on the following two key parts: two-threshold detection and iteration detection. For the two-threshold detection, we make theoretical analysis about the establishment of the two thresholds. And for the iteration detection, a novel spectrum sensing algorithm is proposed to improve the detection performance in low SNR. The iteration algorithm utilizes the following property to improve the signals SNR: the variance of the mean of independent and identity Gaussian random variables is of one variables variance. The simulation results indicate that the proposed MD scheme outperforms the traditional cooperative scheme both on the detection performance and the detection time significantly.