Changxing Pei

Belief Propagation Based Cooperative Compressed Spectrum Sensing in Wideband Cognitive Radio Networks

Wideband spectrum sensing in heterogenous cognitive radio networks has two significant challenges to tackle. One is the spectrum acquisition in the wideband scenario due to the limited sampling capability; the other is how to collaborate among the secondary users. Compressed spectrum sensing provides a powerful approach to acquire wideband signal. Moreover, most cooperative spectrum sensing methods assume that all the secondary users experience the same occupancy of primary users, which may be infeasible in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose a probabilistic graphical model to represent and fuse multi-prior information from one hop neighboring secondary users. Belief propagation (BP) is used for the statistical inference of the spectrum occupancy. Numerical simulation results demonstrate that the proposed BP based cooperative compressed spectrum sensing can effectively achieve cooperation in heterogenous environments and improve performance of compressed spectrum sensing under a low sampling rate and low signal-to-noise ratio (SNR), compared with the other distributed cooperative compressed sensing methods.

Optimal Training Design for Individual Channel Estimation in Two-Way Relay Networks

This correspondence considers the optimal training design in a classical three-node amplify-and-forward two-way relay network (TWRN) that targets at estimating the individual channel between each source node and the relay node. The transmission environment is assumed to be frequency selective and the orthogonal-frequency-division multiplexing (OFDM) modulation is adopted. We derive the Bayesian Cramér-Rao bound (CRB) for the individual channel estimation, from which the optimal training is obtained. Extensive numerical results are provided to corroborate the proposed studies.

Segment Training Based Individual Channel Estimation in One-Way Relay Network with Power Allocation

In this paper, we design a segment training based individual channel estimation (STICE) scheme in the classical three-node it amplify-and-forward (AF) one-way relay network (OWRN). The linear minimum mean-square-error (LMMSE) channel estimator is used to obtain a good initialization, and an iterative maximum a posteriori (MAP) channel estimator is developed to improve the estimation accuracy. We then investigate the underlying power allocation at the relay node both to minimize the mean-square-error (MSE) of the individual channel estimation and to maximize the average effective signal-to-noise ratio (AESNR) of the data detection. The closed-form Bayesian Cramér-Rao Bound (CRB) is also derived to evaluate the proposed algorithm. Finally, numerical results are provided to corroborate the proposed studies.

Space-Time Bayesian Compressed Spectrum Sensing for Wideband Cognitive Radio Networks

Wideband spectrum sensing in cognitive radio networks remains an open challenge due to wideband spectrum acquisition implementation. Compressed spectrum sensing provides a powerful approach to acquire wideband signals. We purpose a probabilistic Space-time Bayesian Compressed Spectrum Sensing (ST-BCSS) to combat the noise in wideband compressed spectrum sensing. We present an informative hierarchical prior probabilistic model to recover the compressed spectrum by exploiting the temporal and spatial prior information. These priori information endows the robustness of spectrum sensing subject to noise and low sampling rate. We present a probabilistic framework to address how to represent, convey and fuse multi-prior information to improve the local compressed spectrum reconstruction. Numerical simulation results demonstrate that the ST-BCSS algorithm improves the performance of compressed spectrum sensing under low sampling rate and low Signal Noise Ratio (SNR), compared with the traditional Basis Pursuit and Orthogonal Matching Pursuit algorithms. A correlation based algorithm for the detection of reconstruction failure due to non-sparse spectrum is also proposed and demonstrated using numerical simulations.

Performance Analysis of TAS/MRC in MIMO Relay Systems With Outdated CSI and Co-Channel Interference

In this paper, we investigate the impact of outdated channel state information (CSI) and multiple co-channel interferers (CCI) on the performance of transmit antenna selection/receiver maximal-ratio combining (TAS/MRC) in a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay network. The tight lower bound and asymptotic expressions for the outage probability are derived. The diversity order of the system with/without outdated CSI in the presence of multiple CCI is further discussed. Numerical and simulation results are presented to validate our analysis and to demonstrate the effect of outdated CSI and multiple CCI on the system performance.

Dynamic Individual Channel Estimation for One-Way Relay Networks With Time-Multiplexed-Superimposed Training

In this paper, we design a time-multiplexed superimposed training (TMST) scheme to estimate the individual channels in amplify-and-forward one-way relay networks (OWRNs) under a doubly selective channel scenario, where the two-phase zero-prefixed block transmission scheme is adopted. The complex-exponential basis expansion model (CE-BEM) is utilized to approximate the channel of each individual hop and results in a coefficient vector with much smaller size, called in-BEM-CV. The channel estimation of the individual channel is then converted into the estimation of in-BEM-CVs. We develop an estimation algorithm with three steps: the standard least squares (LS) estimator, the time domain or the fast Fourier transform (FFT)-based decoupler, and the iterative LS-based refiner. We also optimize the training parameters, including the number, the position, and the power allocation of the pilot clusters by minimizing the estimation mean square error (MSE), and derive one performance lower bound for the proposed algorithm. Finally, numerical results are provided to corroborate the proposed studies.

Study of Segment Training Based Individual Channel Estimation for Two-Way Relay Network

In this letter, we design a segment training based individual channel estimation (STICE) scheme in the classical three-node it amplify-and-forward two-way relay network (TWRN). We resort to linear minimum-mean-square-error (LMMSE) estimators to achieve the in-channel estimation without sign ambiguity through two consecutive segments. We derive the mean-square-error (MSE) of the individual channel estimation, from which the optimal training and the optimal relays power allocation are obtained. Numerical results are provided to corroborate the provided studies.

Channel estimation for two-way relay networks over doubly-selective channels with time-multiplexed-superimposed training

In this paper, we adopt the time-multiplexed-superimposed training and investigate channel estimation for amplify-and-forward (AF) two-way relay network (TWRN) under doubly-selective channel scenario. With the aid of the complex-exponential basis-expansion-model (CE-BEM), we first develop the estimation model for BEM coefficient-vectors (BEM-CVs) of the individual channels between both sources and the relay. A two-step coarse estimator is proposed to obtain the BEM-CVs of the individual channels. Finally, numerical results are provided to corroborate the above studies.

Performance Analysis of Multiuser Two-Way AF Relaying Networks with Antenna Correlation

In this paper, we investigate the effect of spatial correlation on the performance of multiuser two-way amplify-and-forward (AF) relaying system. The lower bound of the sum rate are derived. Furthermore, the asymptotic expressions of the sum rate are analyzed when the number of mobile stations K is sufficiently large. Analysis and simulation results indicate that the achievable sum rates depend on the degree of spatial correlation and the number of mobile stations K. Especially, our results reveal the impact of the relay location with unbalanced hops on the sum rates.

Spectrum Prediction via Temporal Conditional Gaussian Random Field Model in Wideband Cognitive Radio Networks

Wideband spectrum sensing remains an open challenge for cognitive radio networks due to the insufficient wideband sensing capability. This paper introduces the theory of Gaussian Markov Random Field to estimate the un-sensed sub-channel status. We set up a measurement system to capture the WiFi spectrum data. With the measurement data, we verify that the proposed model of Temporal Conditional Gaussian Random Field can efficient estimate the sub-channel status.