Baoyu Tian

A novel OFDM channel estimation method based on Kalman filtering and distributed compressed sensing

Channel estimation is important for coherent detection in orthogonal frequency-division multiplexing (OFDM) systems. Current frequency-domain Kalman filtering (FDKF) channel tracking method requires a large number of pilots, which reduces the spectral efficiency of the system and increases the complexity. In this paper, in order to solve this problem, a new channel estimation method based on the recent methodology of distributed compressed sensing (DCS) and FDKF is proposed. By exploiting the sparse attribute of OFDM channels and introducing DCS, the number of pilots could be reduced greatly, which means more resources are saved for data transmission. Moreover, simulations indicate the proposed method achieves a better performance than conventional FDKF and least square (LS) method.

Graph-based spectrum sharing for multiuser OFDM Cognitive Radio Networks

Spectrum sharing in OFDM-based Cognitive Radio Networks (CRN) is investigated by using graph theory. We consider the networks in which a set of secondary links (SLs) make underlay access to support single-hop ad hoc transmission. Spectrum sharing must be carried out in SLs so that no excessive interference is caused to links of the primary network and maximizes the total achievable rates of SLs. We divide the spectrum sharing into two stages: coarse-scale interference mitigation and SINR-based channel mapping. Interference mitigation is done through constructing weighted undirected interference graph and applying k-max cut method. Then we perform channel-cluster mapping, which takes instantaneous channel quality into consider, on the basis of interference mitigation. Low-complexity heuristic algorithms are proposed to effectively solve the problems of each stage. Numerical simulations for various scenarios are executed to show the superior performance of the proposed schemes.

Channel estimation based on distributed compressed sensing in amplify-and-forward relay networks

Abstract In orthogonal frequency-division multiplexing (OFDM) amplify-and-forward (AF) relay networks, in order to exploit diversity gains over frequency-selective fading channels, the receiver needs to acquire the knowledge of channel state information (CSI). In this article, based on the recent methodology of distributed compressed sensing (DCS), a novel channel estimation scheme is proposed. The joint sparsity model 2 (JSM-2) in DCS theory and simultaneous orthogonal matching pursuit (SOMP) are both introduced to improve the estimation performance and increase the spectral efficiency. Simulation results show that compared with current compressed sensing (CS) methods, the estimation error of our scheme is reduced dramatically in high SNR region while the pilot number is still kept small.

Pilot-aided channel estimation method based on compressed sensing and Kalman filtering in OFDM systems

In this paper, by combining CS and TDKF, we propose a novel channel estimation scheme for OFDM time-variant channels. Compared with conventional TDKF method, our scheme is more practical as it need not to know the delays of multipath previously. Numerical results proved that compared with LS method, both the MSE and the BER performance are improved in our scheme.

Design and analysis of lossy source coding of Gaussian sources with finite-length polar codes

Polar codes are proven to achieve the rate distortion bound of Gaussian sources under the condition that the size of reconstruction alphabet and code length grow to infinity. However, this condition cannot be satisfied in practice. In this paper, we propose a practical source coding scheme based on multilevel polar codes, called polar coded quantization (PCQ). In this scheme, extended reconstruction alphabet, set-partition (SP) labeling and successive cancellation (SC) (or its improved) encoding algorithm are combined to approach the rate distortion bound. Furthermore, an efficient upper bound of encoding rate for a given distortion is derived to guide the practical design of PCQ. Simulation results show that PCQ provides a flexible and constructive framework to efficiently approximate the rate distortion bound of Gaussian sources.

A practical compress-and-forward relay scheme based on superposition coding

Compress and forward (CF) is one of the protocols in cooperative communication and has drawn much attention. In CF scheme, the relay compresses and transmits the received signals to the destination, where Wyner-Ziv coding is considered as an efficient way to compress the signal with side information available. Since the current research of superposition coding for Wyner-Ziv problem only remains in theory, we design a practical CF scheme in half-duplex mode by using the technique of superposition coding and provide specific coding strategy for it. The relay compresses the received signal by the quantization method based on superposition coding, and then the destination performs a joint decoding to recover the original message. Simulations show that our proposed scheme outperforms the conventional CF scheme with scalar quantization at the relay. This coding strategy can be easily extended to the scenarios of fading channels, which are more practical in wireless communication.

Polar codes for soft decode-and-forward in half-duplex relay channels

Soft decode-and-forward (DF) can combine the advantages of both amplify-and-forward and hard DF in relay channels. In this paper, we propose a low-complexity soft DF scheme based on polar codes, which features two key techniques: a low-complexity cyclic redundancy check (CRC) aided list successive cancellation (CALSC) decoder and a soft information calculation method. At the relay node, a low-complexity CALSC decoder is designed to reduce the computational complexity by adjusting the list size according to the reliabilities of decoded bits. Based on the path probability metric of the CALSC decoder, we propose a method to compute the soft information of the decoded bits in CALSC. Simulation results show that our proposed scheme outperforms the soft DF based on low-density parity-check codes and the soft DF with belief propagation or soft cancellation decoder, especially in the case when the source-relay channel is at the high signal-to-ratio region.

A novel Bayesian Compressed Sensing algorithm using sparse tree representation

Compressed Sensing (CS) is a novel emerged theory in the last several years in the area of signal processing. CS could recover the signal correctly by sampling a sparse signal below the Nyquist rate. Bayesian Compressed Sensing (BCS) is a new framework in CS which recovery performance is proved to be close to L0-norm solution. Recent studies have recognized that in many multiscale bases such as wavelets, signals of interest have not only few significant coefficients, but also a well-organized tree structure of those significant coefficients. In this paper, we exploit the tree structure as additional prior information to the framework of the BCS, and then propose a novel BCS algorithm for signal reconstruction with limited number of measurements. Simulation results indicate that exploiting the proposed BCS algorithm using the sparse tree representation could reduce the required number of iterations greatly, and achieve better reconstruction as well as faster iteration speed compared to original BCS algorithm.