Linhua Zheng

First Path Detection Using Rank Test in IR UWB Ranging with Energy Detection Receiver under Harsh Environments

To estimate time of arrival (TOA) in impulse radio (IR) ultra wideband (UWB) ranging under harsh environments, the rank test is adopted in the study to detect the first path (FP) in dense multipath with energy detection (ED) receiver. Most TOA estimation methods work well in the additive white Gaussian noise (AWGN). Actually, the background noise could be non-Gaussian. Moreover, the received signal could be polluted by outliers. In these cases, traditional threshold based methods perform badly, as there may be extraordinary large samples in the output of the receiver. These samples will interfere with the detection of the sample containing the FP. A novel and practical method based on the rank test is proposed to deal with these problems. The FP detection is based on the ranks of the received data samples instead of the samples. Simulations are carried out to demonstrate its effectiveness in the IEEE 802.15.4a channels.

Sensing Transmission Tradeoff Over Penalty for Miss Detection in Cognitive Radio Network

In this paper, a challenge issue ‘sensing and data transmission trade-off’ is addressed. Different from existing efforts, penalty is taken as a rule of regulation for spectrum sensing and access in order to possibly protect primary’s activity. Specifically, secondary user needs to pay rental fee to primary user for opportunistic access. However, penalty will be charged from secondary user as well if it has transmitted on the miss estimated spectrum, in which, an negative secondary raw utility will be generated when the penalty value is properly decided. Thus the scheme will further force secondary user to improve its sensing accuracy and select the best transmission strategy when compared with conventional sensing access model. Based on the scheme, in this research, the bounds for proper rental and penalty prices are derived. Further, within both proper prices region, an optimization problem for setting sensing duration and transmission power is formulated. Through investigation, convexity of the problem is hard to be obtained at global view. But thanks to the special property that the problem is local convex when fixing any one of targeted variables, a Lagrange method based iterative algorithm is presented to find solution within a decent convergence of no more than 10 iterations.

Application of time reversal for TOA estimation in UWB ranging under dense multipath channel

In most time-based ultra-wide bandwidth (UWB) localization systems, ranging is usually accomplished by the time-of-arrival (TOA) estimation of the first path, which is more challenging in dense multipath channels. This paper proposes a novel time reversal (TR) based method for TOA estimation in UWB ranging. It uses a time reversed version of channel impulse response (CIR) to compensate for the highly cluttered environments. After the time reversed waveform is retransmitted into the same channel, the performance of TOA estimation will be significantly improved by simply search the maximum peak in the output of the receiver. The advantages of TR based method are verified by simulations using the classical IEEE 802.15.4a channel models. Results show that the bias and root mean square error (RMSE) of TR based method are greatly reduced compared to the usually used maximum likelihood method.

Modified Gradient Descent Bit-Flipping Decoding for Low-Density Parity-Check Codes

In this paper, we modify the gradient descent bit-flipping (GDBF) decoding of low-density parity-check codes based on the syndrome. Firstly, the syndrome weight is utilized to detect the decoding loop, which seriously effects the performance of GDBF decoding. Then the syndrome information is introduced to update the reliability of the flipped bit nodes. Since the modified GDBF, denoted as MGDBF, only uses the syndrome weight and syndrome information, there is small complexity increased. Simulation results indicate that the two modifications bring about significant improvement in error-rate performance. For single MGDBF decoding, its performance is not only better than that of GDBF, but also is better than that of noisy GDBF. For multi MGDBF decoding, it can obtain fast convergence rate and good performance by employing the appropriate adaptive threshold scheme.

Design of Spread Spectrum TTaC Link for UAV-Swarm Based on Multi - path

In this paper, a kind of TT&C link is designed for the TT&C system of UAV-Swarm, and the characteristics of code division multiple access are used to realize the TT&C of the mass unmanned aerial vehicle. Due to the effects of noise and multipath fading in mobile wireless channels, multipath fading can enhance the received signal power through Rake receiver technology and improve the reliability of system transmission by equal gain combining.Under the condition of low SNR, the relatively low bit error rate can be achieved.

An escaping scheme for gradient descent bit-flipping decoding of LDPC codes

The gradient descent bit-flipping (GDBF) decoding algorithm offers an attractive tradeoff between performance and complexity compared with the other bit-flipping (BF) decoding algorithms. However, the performance of GDBF decoding is seriously effected by infinite decoding loop, which always results in decoding failure when maximal iteration number is reached. To avoid the decoding loop and improve the performance, an efficient escaping scheme is proposed for the GDBF algorithm in this paper. This scheme firstly introduces syndrome weight to detect the decoding loop. If a decoding loop is detected, the escaping mechanism works, where only the bit which can decrease the syndrome weight most efficiently is selected to be flipped. In addition, the excluding set is introduced to further prevent the local maximum. Simulation results show that the GDBF algorithm can obtain about 1 dB code gain by utilizing this scheme. Compared with noise GDBF, it has 0.1dB performance loss as well as lower complexity.

Hybrid iterative decoding for LDPC codes based on gradient descent bit-flipping algorithm

Gradient descent bit-flipping (GDBF) decoding for low-density parity-check (LDPC) codes has gained great attentions because of low complexity and relatively good performance. However, the performance of the GDBF decoding is still much poorer than that of the MS algorithm. To further improve the performance, the iterative decoding process is introduced to GDBF decoding in this paper. At the end of each iteration, the latest information is used to update the reliability of the flipped bit, by which its reliability is greatly improved. Simulation results indicate that the proposed algorithm has about 0.4 dB performance gains compared to GDBF. Meanwhile, there is small computational complexity increased.

An efficient scheduling scheme for layered belief propagation decoding of regular LDPC codes

Schedule plays an important role in layered belief propagation (BP) decoding of low-density parity-check (LDPC) codes. It affects the error-rate performance as well as convergence rate of the decoder. In this paper, an efficient scheduling scheme is proposed to arrange the updating order of check nodes for layered BP decoder of regular LDPC codes. This scheme selects check node one at a time based on the reliability of the variable nodes. Specifically each time the check node with maximum number of low-reliability neighbors is selected. For simplicity, we introduce the updating state instead of the real-time messages to represent variable-node reliability. Simulation results on regular LDPC codes prove that the proposed scheduling scheme can obtain efficient schedules with fast convergence and good error-rate performance.

Prediction and separation of synchronous-networking frequency hopping signals based on RBF neural network

In this paper, a novel method of prediction and separation of Synchronous-networking frequency hopping (FH) signals is proposed. By choosing suitable chaotic sequences which have pseudo-orthogonal property and global phase diagram with single mapping path, a Radial Basis Function (RBF) neural network can be trained for the prediction and separation of multi FH signals. First, the performance of chaotic sequence is analyzed, based on which the inputs and outputs for the training of RBF neural network are selected. Then the algorithm of Orthogonal Least Square (OLS) is applied for the predictor and separator. Simulation results show that the algorithm can effectively predict and separate the synchronous-networking FH signals.

Efficient Puncturing Scheme for Irregular LDPC Codes Based on Serial Schedules

In practice, low-density parity-check (LDPC) codes are usually decoded by serial schedules. However, the existing schemes for rate-compatible puncturing LDPC codes are proposed on the assumption that the flooding schedules are employed in decoding algorithm. In this letter, an efficient puncturing scheme is proposed for irregular LDPC codes based on serial schedule. The scheme selects variable nodes to be punctured one at a time. At each time, a check node is selected firstly based on the idea that the punctured variable nodes are allocated evenly to all the check nodes. Then the puncturing variable node is selected among its neighbors. Furthermore, the order of the check nodes in the selection serves as the updating order in the serial schedule, which ensures that all the punctured codes can be recovered at the first iteration. Simulation results demonstrate that the scheme performs better for a wide range of code rates compared with the existing puncturing scheme, especially for high-rate punctured codes.