Linear and non‐linear space diversity combining algorithms over fading channels in TDMA systems

This paper investigates the performances of various adaptive algorithms for space diversity combining in time division multiple access (TDMA) digital cellular mobile radio systems. Two linear adaptive algorithms are investigated, the least mean square (LMS) and the square root Kalman (SRK) algorithm. These algorithms are based on the minimization of the mean‐square error. However, the optimal performance can only be obtained using algorithms satisfying the minimum bit error rate (BER) criterion. This criterion can be satisfied using non‐linear signal processing techniques such as artificial neural networks. An artificial neural network combiner model is developed, based on the recurrent neural network (RNN) structure, trained using the real‐time recurrent learning (RTRL) algorithm. It is shown that, for channels characterized by Rician fading, the artificial neural network combiners based on the RNN structure are able to provide significant improvements in the BER performance in comparison with the linear techniques. In particular, improvements are evident in time‐varying channels dominated by inter‐symbol interference. Copyright © 2001 John Wiley & Sons, Ltd.     

衰落信道下卷积码的软判决译码

本文将交织、信道输出软信息、信道特征软信息相结合,研究Rayleigh衰落信道下卷积码的Viterbi软判决译码,在利用和不利用信道状态信息(CSI)两种情况下给出软判决的支路度量计算公式和性能仿真结果,计算了卷积码的性能上界;研究了解调器输出量化门限(量化级数目、量化间距)对译码性能的影响.结果与硬判决译码及AWGN信道的相关结论进行了比较.     

Uplink performance of slow frequency-hop multiple-access networks using binary DPSK

The uplink performance of synchronous and asynchronous slow frequency-hop spread-spectrum multiple-access (SFHSS-MA) networks transmitting L bits per hop using binary differential phase shift keying (BDPSK) is analyzed under the additive white Gaussian noise (AWGN) and Rayleigh fading channels. Analytic expressions for the average conditional bit error probabilities given a hop is hit by K' interfering users are derived. Results show that SFHSS-MA networks using BDPSK achieve nearly twice the maximum normalized network throughput compared to networks using BFSK under both AWGN and Rayleigh fading channels.     

Quantizability and learning complexity in multilayer neuralnetworks

The relationship between quantizability and learning complexity in multilayer neural networks is examined. In a special neural network architecture that calculates node activations according to the certainty factor (CF) model of expert systems, the analysis based upon quantizability leads to lower and also better estimates for generalization dimensionality and sample complexity than those suggested by the multilayer perceptron model. This analysis is further supported by empirical simulation results     

Decision feedback differential phase detection of M-ary DPSKsignals

Multiple-symbol differential phase detection (DFDPD) based on decision feedback of past detected symbols is presented for M-ary DPSK modulation. Adopting a Gaussian phase noise assumption, we obtain the a posteriori joint probability density function (PDF) of the outputs of L DPD defectors of orders of 1 to L symbols and derive a DF-DPD algorithm which is based on feeding back the L-1 past detected symbols and minimizing the sum of phase errors of L DPD detectors. A practical implementation of the DF-DPD receiver is presented that uses a single conventional (one-symbol) DPD detector. The bit error rate (BER) performance in an additive white Gaussian noise (AWGN) channel is analyzed taking into account decision error propagation. Performance improvements are evaluated by computer simulations in AWGN and Rayleigh fading channels     

Polynomial perceptrons and their applications to fading channelequalization and co-channel interference suppression

This paper investigates the behaviors of polynomial perceptrons and introduces a fractionally spaced recursive polynomial perceptron with low complexity and fast convergence rate. The nonlinear mapping ability of the polynomial perceptron is analyzed. It is shown that a polynomial perceptron with degree L(⩾4) satisfies the Stone-Weierstrass theorem and can approximate any continuous function to within a specified accuracy. Moreover, the nonlinear mapping ability of a polynomial perceptron with degree L is similar to that of the three-layer perceptron with one hidden layer for time same number of neurons in the input layer. The nonlinear mapping ability of the fractionally spaced recursive polynomial perceptron is also presented. Applications of polynomial perceptrons for fading channel equalization and co-channel interference suppression in a 16-level quadrature amplitude modulation receiver system are considered. Computer simulations are used to evaluate and compare the performance of polynomial perceptron (PP) and fractionally spaced bilinear perceptron (FSBLP) with that of the synchronous decision feedback multilayer perceptron (SDFMLP), fractionally spaced decision feedback multilayer perceptron (FSDFMLP), and the conventional decision feedback equalizer (DFE). The results show that the performance of the fractionally spaced bilinear perceptron is clearly superior to that of the other structures     

Conditional distribution learning with neural networks and itsapplication to channel equalization

We present a conditional distribution learning formulation for real-time signal processing with neural networks based on an extension of maximum likelihood theory-partial likelihood (PL) estimation-which allows for (i) dependent observations and (ii) sequential processing. For a general neural network conditional distribution model, we establish a fundamental information-theoretic connection, the equivalence of maximum PL estimation, and accumulated relative entropy (ARE) minimization, and obtain large sample properties of PL for the general case of dependent observations. As an example, the binary case with the sigmoidal perceptron as the probability model is presented. It is shown that the single and multilayer perceptron (MLP) models satisfy conditions for the equivalence of the two cost functions: ARE and negative log partial likelihood. The practical issue of their gradient descent minimization is then studied within the well-formed cost functions framework. It is shown that these are well-formed cost functions for networks without hidden units; hence, their gradient descent minimization is guaranteed to converge to a solution if one exists on such networks. The formulation is applied to adaptive channel equalization, and simulation results are presented to show the ability of the least relative entropy equalizer to realize complex decision boundaries and to recover during training from convergence at the wrong extreme in cases where the mean square error-based MLP equalizer cannot     

Decision-feedback blind adaptive multiuser detector for synchronousCDMA system

We develop a blind adaptive multiuser detector for synchronous code-division multiple access (CDMA) with a noise-whitening filter. The triangular structure of the noise-whitened model ensures complete resolution of detection ambiguities. To further improve the symbol error probability performance, we introduce decision feedback in our detector similar to the decorrelating derision-feedback detector (DDFD), thus forming the decision-feedback blind adaptive multiuser detector (DFBD). Simulations indicate that the performance of the DFBD is very close to that of the DDFD in additive white Gaussian noise (AWGN) channels. In Rician fading channels, the DFBD can track the slowly varying channels well and has a symbol error probability performance approaching that of the DDFD, which requires the knowledge of users' energies. The blind adaptive and decision-feedback blind adaptive multiuser detectors proposed here do not, however, require that knowledge     

EEG-based lapse detection with high temporal resolution

A warning system capable of reliably detecting lapses in responsiveness (lapses) has the potential to prevent many fatal accidents. We have developed a system capable of detecting lapses in real-time with second-scale temporal resolution. Data was from 15 subjects performing a visuomotor tracking task for two 1-hour sessions with concurrent electroencephalogram (EEG) and facial video recordings. The detector uses a neural network with normalized EEG log-power spectrum inputs from two bipolar EEG derivations, though we also considered a multichannel detector. Lapses, identified using a combination of video rating and tracking behavior, were used to train our detector. We compared detectors employing tapped delay-line linear perceptron, tapped delay-line multilayer perceptron (TDL-MLP), and long short-term memory (LSTM) recurrent neural networks operating continuously at 1 Hz. Using estimates of EEG log-power spectra from up to 4 s prior to a lapse improved detection compared with only using the most recent estimate. We report the first application of a LSTM to an EEG analysis problem. LSTM performance was equivalent to the best TDL-MLP network but did not require an input buffer. Overall performance was satisfactory with area under the curve from receiver operating characteristic analysis of 0.84 +/- 0.02 (mean +/- SE) and area under the precision-recall curve of 0.41 +/- 0.08.     

MLP equaliser for frequency selective time-varying channels

A multilayer perceptron based equaliser with RLS adaptation algorithm is presented. Its performance is evaluated in a communications system using GMSK modulation with frequency selective Rayleigh fading channel of six paths. A bit error rate performance comparison shows that the MLP structure has better performance than the traditional decision feedback equaliser     

Multiuser detection of time-hopping PPM UWB system in the presence of multipath fading

Ultra-wideband (UWB) impulse radio (IR) systems are currently being considered for several applications due to their attractive features that include low-power carrierless and ample multipath diversity. Among the various modulation and multiple-access schemes, time-hopping (TH) pulse position modulation (PPM) is a popular technique in application. Most past works rely on strict power control and perform single-user detection (matched filtering) on the desired signal. This paper aims to apply multiuser detection techniques in binary PPM (BPPM) UWB IR multiple-access systems. Moreover, we consider frequency-selective multipath fading channels to account for the wireless cellular environment. A class of linear multiuser detectors (LMDs) is applied to extract the information bits while eliminating multiuser interference (MUI) in the presence of multipath fading. Simulation results are provided to compare the performance of different LMDs.     

Neural networks for frequency line tracking

This paper investigates the application of neural networks to frequency line tracking. Recently, hidden Markov models (HMM's) have been successfully applied to this problem, and here, we study a neural network architecture called Mnet, which is based on an underlying Markov model representation. A supervised learning algorithm is developed for Mnet, and a method of analytically deriving the connection weights for the Mnet is also mentioned. Two more conventional neural networks are also studied; a multilayer feedforward network and a multilayer network with feedback. The simulation results show that all three neural networks are comparable in performance to a hidden Markov model when applied to the frequency line tracking problem     

认知车载网的频谱感知性能分析

认知车载网中不同的衰落传播信道会影响频谱感知的性能.为了描述不同信道下的频谱感知性能,分析了加性高斯白噪声信道、瑞利衰落信道和Nakagami-m衰落信道条件下的频谱感知单用户能量检测概率和协作能量检测概率,重点是Nakagami-m衰落信道、检测衰落因子、次用户的数量和信噪比这几个参数对协作频谱感知性能的影响.仿真结果表明,通过对不同信道特性的准确了解能够帮助车辆在不同衰落信道环境下提高频谱的检测概率.     

Direction of arrival estimation based on phase differences usingneural fuzzy network

A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed. The conventional DOA estimation method such as MUSIC and MLE, are computationally intensive and difficult to implement in real time. To attack these problems, neural networks have become popular for DOA estimation. However, the normal neural networks such as the multilayer perceptron (MLP) and radial basis function network (RBFN) usually produce the extra problems of low convergence speed and/or large network size (i.e., the number of network parameters is large). Also, the may to decide the network structure is heuristic. To overcome these defects and take use of neural learning ability, a powerful self-constructing neural fuzzy inference network (SONFIN) is used to develop a new DOA estimation algorithm. By feeding the PDs of the received radar-array signals, the trained SONFIN can give high-resolution DOA estimation. The proposed scheme is thus called PD-SONFIN. This new algorithm avoids the need of empirically determining the network size and parameters in normal neural networks due to the powerful on-line structure and parameter learning ability of SONFIN. The PD-SONFIN can always find itself an economical network size in the fast learning process. Our simulation results show that the performance of the new algorithm is superior to the RBFN in terms of convergence accuracy, estimation accuracy, sensitivity to noise, and network size     

采用基于互补序列分组编码的OFDM系统性能分析与仿真

为了减小正交频分复用（OFDM）信号的峰值-平均值功率之比（PAPR），本文利用互补序列和Reed-Muller码的关系，详细提出了一种构造互补序列的分组编码方法的具体实现方案。分析了其在AWGN和选频衰落信道中的性能，并做了相应的仿真。仿真结果表明，编码后每个OFDM信号的最大PAPR不超过3dB；采用该编码方法的OFDM在AWGN中当信噪比不到11dB时就可以实现BER为10^-6，在衰落信道中如果采用软判决译码，则当信噪比达到20dB左右时可以实现BER为10^-3。     

Adaptive coded modulation for fading channels

We apply coset codes to adaptive modulation in fading channels. Adaptive modulation is a powerful technique to improve the energy efficiency and increase the data rate over a fading channel. Coset codes are a natural choice to use with adaptive modulation since the channel coding and modulation designs are separable. Therefore, trellis and lattice codes designed for additive white Gaussian noise (AWGN) channels can be superimposed on adaptive modulation for fading channels, with the same approximate coding gains. We first describe the methodology for combining coset codes with a general class of adaptive modulation techniques. We then apply this methodology to a spectrally efficient adaptive M-ary quadrature amplitude modulation (MQAM) to obtain trellis-coded adaptive MQAM. We present analytical and simulation results for this design which show an effective coding gain of 3 dB relative to uncoded adaptive MQAM for a simple four-state trellis code, and an effective 3.6-dB coding gain for an eight-state trellis code. More complex trellis codes are shown to achieve higher gains. We also compare the performance of trellis-coded adaptive MQAM to that of coded modulation with built-in time diversity and fixed-rate modulation. The adaptive method exhibits a power savings of up to 20 dB     

一种基于等效容量的链路性能预测方法

该文提出一种在信道复杂变化时对链路性能进行准确预测的方法。在介绍了一种基于Shannon信道容量的量度的基础上,引入了一个新的能够反应信道衰落速度的惩罚因子Q。对于不同的调制方式和编码速率,给出了Q 与信道变化程度的关系式,以及需要参考的AWGN信道下的性能曲线。仿真结果表明该方法能够实时地反映出信道的变化程度,从而使得预测更加准确。该文虽以Turbo码性能的预测为例,但是很容易就可以推广到其他链路级的情况。     

A Comparison of Energy Detectability Models for Cognitive Radios in Fading Environments

In this work, we first analyze the accuracy of different energy detector models in approximating the exact solution in AWGN. These models motivate us to develop approximation analysis to address energy detection for fading channels. Our analysis develops approximation that has almost the same performance as the exact solution in Rayleigh channels. Our new model is simple enough to derive the relationship between the required number of samples (N) and the signal-to-noise ratio for a single Rayleigh channel similar to the one obtained for AWGN channels. We also define a fading margin for link budget calculations that relates N in fading channels to AWGN channels. Furthermore, we analyze the impact of multiple antennas for cognitive radios considering two receiver diversity schemes and quantify the improvement in performance regarding this margin. All the analytical results derived in this paper are verified by simulations. Finally, we have implemented and verified energy detection models in our multiple antenna wireless testbed.     

衰落环境下一种高能效协作频谱感知算法

在认知无线网络中,对于电池供电的认知设备,如何高效地利用其能量资源极为重要。在将能量效率(能效)定义为认知网络频谱利用效率和平均功率消耗之比的基础上,提出了一种高能效优化算法,在加性高斯白噪声(AWGN)信道、Rayleigh衰落信道和Nakagami衰落信道条件下使融合中心门限达到最优,并求得了最佳的参与协作的用户数。通过蒙特卡洛仿真对认知网络的能效进行了性能评估,结果表明所提算法能有效提升认知网络的能量效率。     

New fractionally spaced recursive polynomials perceptron model for adaptive M-QAM digital mobile radio reception

A new fractionally spaced recursive polynomial perceptron (FSRPP) model for adaptive M-QAM digital mobile radio reception is described, which can adaptively equalise the multipath fading channels and reject non-Gaussian cochannel interference (CCI) simultaneously. Experimental results obtained are satisfactory, which shows that FSRPP with lower computational burden and faster convergence rate can perform more efficiently than polynomial perceptron (PP) and multilayer perceptron (MLP) in the presence of multipath fading of channels and non-Gaussian interference.<>