Adaptive Signaling Based on Statistical Characterizations of Outdated Feedback in Wireless Communications

Wireless links form a critical component of communication systems that aim to provide ubiquitous access to information. However, the time-varying characteristics (or "state") of wireless channels caused by the mobility of transmitters, receivers, and objects in the environment make it difficult to achieve reliable communication. Adaptive signaling exploits any channel state information (CSI) available at the transmitter to provide the potential to significantly increase the throughput of wireless links and/or greatly reduce the receiver complexity. As such, adaptive signaling has attracted significant research interest in the last decade and has found application in numerous commercial wireless systems, ranging from cellular data systems to wireless local area networks (WLANs). However, one of the great challenges of wireless communications is that it is difficult to obtain perfect CSI due to the inherently noisy and outdated nature of CSI available at the transmitter. Over the last decade, we have championed the idea of choosing the appropriate transmitted signal based on statistical models for the current channel state conditioned on the channel measurements. In this semi-tutorial paper, we first review how this class of methods has been developed for single-antenna systems, and then present novel recent designs for multiple-antenna systems. Key to the development in each case is the development of the error characterization given the outdated estimates and the use of such to allocate data rate and power over time and possibly space. In general, the focus is on rate allocation, while power allocation is done through a pruning method. Numerical results will demonstrate in both the single-antenna and multiple-antenna cases that such an approach provides a robust method for improving system data rate versus the standard practice of employing link margin to compensate for such uncertainties.     

Blind equalization of IIR channels using hidden Markov models andextended least squares

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization     

A decision theoretic approach for channel ranking in crowded unlicensed bands

Interference is disruptive to the operation of wireless sensor networks (WSNs) in unlicensed bands as wireless systems proliferate on the spectrum. The design of a spectrum sharing scheme for WSNs to enable coexistence with geographically collocated heterogeneous wireless systems having multiple parallel interfering channels is a persistent challenge. In this context, interference identification and channel ranking in terms of spectrum access opportunities are addressed in this paper. The goal is to develop a low complexity channel ranking algorithm from channel energy measurements at sensors when a packet-reception-ratio to signal-to-interference-and-noise-ratio (PRR-SINR) interference model is unavailable at network initialization phase. The interference characterizing estimators, temporal occupancy and strength level of a channel, are proposed for interference identification. The effectiveness of the estimators is tested on a sensor platform at 2.4 GHz ISM band under interference from WLAN. Subsequently, the impact of the interference estimators on a channel quality from a receiver perspective is determined with a decision theoretic approach. The estimators are weighted according to their influence on the fitness of a channel and channel ranking is established. The proposed channel ranking achieves a significant gain over heuristic channel ranking (HCR) and gives an accurate interference profile of the channels.     

Parameter and State Estimation Algorithm for a State Space Model with a One-unit State Delay

This paper derives a state estimation based parameter identification algorithm for state space systems with a one-unit state delay. We derive the identification model of an observability canonical state space system with a one-unit state delay. The key is to replace the unknown states in the parameter estimation algorithm with their state estimates and to identify the parameters of the state space models. Finally, two illustrative examples are given to show the effectiveness of the proposed algorithm.     

Closed-form blind channel identification and source separation inSDMA systems through correlative coding

We address the problem of blind identification of multiuser multiple-input multiple-output (MIMO) finite-impulse response (FIR) digital systems. This problem arises in spatial division multiple access (SDMA) architectures for wireless communications. We present a closed-form, i.e., noniterative, consistent estimator for the MIMO channel based only on second-order statistics. To obtain this closed form we introduce spectral/correlation asymmetry between the sources by filtering each source output with adequate correlative filters. Our algorithm uses the closed form MIMO channel estimate to cancel the intersymbol interference (ISI) due to multipath propagation and to discriminate between the sources at the wireless base station receiver. Simulation results show that, for single-user channels, this technique yields better channel estimates in terms of mean-square error (MSE) and better probability of error than a well-known alternative method. Finally, we illustrate its performance for MIMO channels in the context of the global system for mobile communications (GSM) system     

FPGA implementation of dynamic channel assignment algorithm for cognitive wireless sensor networks

The reliability of wireless sensor networks (WSNs) in industrial applications can be thwarted due to multipath fading, noise generated by industrial equipment or heavy machinery and particularly by the interference generated from other wireless devices operating in the same spectrum band. Recently, cognitive WSNs (CWSNs) were proposed to improve the performance and reliability of WSNs in highly interfered and noisy environments. In this class of WSN, the nodes are spectrum aware, that is, they monitor the radio spectrum to find channels available for data transmission and dynamically assign and reassign nodes to low-interference condition channels. In this work, we present the implementation of a channel assignment algorithm in a field-programmable gate array, which dynamically assigns channels to sensor nodes based on the interference and noise levels experimented in the network. From the results obtained from the performance evaluation of the CWSN when the channel assignment algorithm is considered, it is possible to identify how many channels should be available in the network in order to achieve a desired percentage of successful transmissions, subject to constraints on the signal-to-interference plus noise ratio on each active link.     

A robust random multiple-access algorithm for packet transmissionsover noisy channels with error memory

In this paper, a noisy slotted channel is considered. It is assumed that channel feedback might be misinterpreted due to the existence of noise on the channel. Furthermore, this disturbance is dependent on the channel state (either good or bad) which varies from slot to slot according to a Markov chain. Consequently, the occurrence of the a channel feedback error is dependent on previous occurrences of errors (i.e., with error memory). Under this assumption, the throughput performance of a random multiple-access algorithm, called the Two-Cell algorithm, is analyzed and the results are compared with the throughputs of the Capetanakis (1979) tree-splitting algorithm operating over the same channels. It is shown that the Two-Cell algorithm retains positive throughputs for all possible values of channel state parameters, and for all practical purposes, it outperforms the Capetanakis algorithm in terms of insensitivity to channel feedback errors     

Discrete Rayleigh fading channel modeling

In order to understand the behaviour of upper‐layer protocols and to design or fine tune their parameters over wireless networks, it is common to assume that the underlying channel is a flat Rayleigh fading channel. Such channels are commonly modeled as finite state Markov chains. Recently, hidden Markov models have also been employed to characterize these channels. In this paper, we study the different models that have been proposed along with the analysis of their validity. We start by presenting some preliminary concepts related to the modeling of the wireless communications channel. We then proceed to introduce finite state Markov channel models (FSMCs) along with the relations between them and the modulation schemes, error control protocols and channel coding. We propose and study the effects of taking into account the fading process in its characterization. We finish with a discussion on hidden Markov models for Rayleigh fading channel modeling. Copyright © 2004 John Wiley & Sons, Ltd.     

Subspace-based noise variance and SNR estimation for MIMO OFDM systems

This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio （SNR） estimation algorithm for Multi-Input Multi-Output （MIMO） wireless Orthogonal Frequency Division Multiplexing （OFDM） systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.     

An average error probability evaluation in a fading environment using finite state Markov channel for LEO satellite communication system

Recent research shows that fading channels have a much larger capacity than anticipated with traditional approaches. This modern view on fading channels encouraged us to characterize these channels more precisely for better identification and use of wireless channel capacity.Since the Markov model is a natural way to approximate a channel with memory, many people have considered finite state first-order Markov modeling for describing a wireless communication channel.In this paper, we first introduce the relationship between a physical fading channel and the corresponding finite state Markov Model (FSMC) in case of low earth orbit (LEO) satellite communication system, which can be used for performance evaluation in an M-order quadrature amplitude-modulation (MQAM) transmission scheme by deriving an analytical expression of average bit error rate in Rayleigh fading channel. By establishing the FSMC, we show that the FSMC is accurate enough to evaluate the performance of MQAM modulation scheme to be implemented on board a LEO satellite communication system.     

基于分数低阶统计量的盲多用户检测算法

多用户检测算法是抑制CDMA系统中多址干扰的重要手段,但广泛存在的非高斯信道噪声会降低以往的基于高斯噪声模型假设的算法性能.本文采用α稳定分布作为噪声模型,提出了基于分数低阶统计量的盲多用户检测算法,并对该算法进行了理论分析.仿真和分析表明,该算法具有很好的韧性,同时适用于高斯噪声和脉冲噪声环境.     

A New Union Bound on the Error Probability of Binary Coded OFDM Systems in Wireless Environments

Orthogonal Frequency Division Multiplexing (OFDM) systems are commonly used to mitigate frequency-selective multipath fading and provide high-speed data transmission. In this paper, we derive new union bounds on the error probability of a coded OFDM system in wireless environments. In particular, we consider convolutionally coded OFDM systems employing single and multiple transmit antennas over correlated block fading (CBF) channels with perfect channel state information (CSI). Results show that the new union bound is tight to simulation results. In addition, the bound accurately captures the effect of the correlation between sub-carriers channels. It is shown that as the channel becomes more frequency-selective, the performance get better due to the increased frequency diversity. Moreover, the bound also captures the effect of multi-antenna as space diversity. The proposed bounds can be applied for coded OFDM systems employing different coding schemes over different channel models.     

Transmit antennae space-time block coding for generalized OFDM inthe presence of unknown multipath

Transmit antenna diversity has been exploited to develop high-performance space-time coders and simple maximum-likelihood decoders for transmissions over flat fading channels. Relying on block precoding, this paper develops generalized space-time coded multicarrier transceivers appropriate for wireless propagation over frequency-selective multipath channels. Multicarrier precoding maps the frequency-selective channel into a set of flat fading subchannels, whereas space-time encoding/decoding facilitates equalization and achieves performance gains by exploiting the diversity available with multiple transmit antennas. When channel state information is unknown at the receiver, it is acquired blindly based on a deterministic variant of the constant-modulus algorithm that exploits the structure of space-time block codes. To benchmark performance, the Cramer-Rao bound of the channel estimates is also derived. System performance is evaluated both analytically and with simulations     

A Novel Three-State ARQ Scheme for Variable Error-Tate Channels

This paper presents a new type of automatic-repeat-request (ARQ) scheme, Three-State ARQ (TS-ARQ), for error control in data transmission over a noisy channel. The new scheme is based on the Go-Back-N (GBN) protocol and uses three different methods of GBN protocols: basic GBN, n-copy GBN and continuous-GBN. The new ARQ model is applicable for channels having the variable noise level going from low through medium until very high levels. As it is known, such wireless channels are to be found in terrestrial and space (satellite) communications. This model is to be used for the estimation of the noise state in the channel and one of the methods is used, depending of the noise level. When the noise level is low GBN-ARQ is used, in the case of the medium noise level the n-copy GBN is used, and if the noise level is high continuous-GBN will be applied. This paper presents the method of determining the parameters and transfer moments from one state to another. An original mathematical model is given, together with evaluation results. These results are compared with the known methods and the conclusion that the described method provides some better performances is drowned. The implementation of this new procedure is simple as described in the flow chart given in the paper.     

A blind multichannel identification algorithm robust to orderoverestimation

Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations     

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in \(2\times 1\) space–time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.     

基于输出信号过采样的RoF下行链路辨识

在分析ROF(Radio over Fiber)系统的组成与下行传输模型的基础上,提出了一种对ROF系统下行链路模型的辨识算法.ROF下行链路由非线性无记忆的光纤链路和一个时变的线性多径无线信道组成,通过对ROF系统输出的失真信号进行过采用,从而辨识出无线信道的传输函数,重构ROF系统的中间信号,最后由系统的输入信号和重构的中间信号估计出ROF系统非线性链路的模型参数.通过仿真分析表明,该算法对ROF系统下行链路模型辨识的有效性.     

A study of vector quantization for noisy channels

Several issues related to vector quantization for noisy channels are discussed. An algorithm based on simulated annealing is developed for assigning binary codewords to the vector quantizer code-vectors. It is shown that this algorithm could result in dramatic performance improvements as compared to randomly selected codewords. A modification of the simulated annealing algorithm for binary codeword assignment is developed for the case where the bits in the codeword are subjected to unequal error probabilities (resulting from unequal levels of error protection). An algorithm for the design of an optimal vector quantizer for a noisy channel is briefly discussed, and its robustness under channel mismatch conditions is studied. Numerical results for a stationary first-order Gauss-Markov source and a binary symmetric channel are provided. It is concluded that the channel-optimized vector quantizer design algorithm, if used carefully, can result in a fairly robust system with no additional delay. The case in which the communication channel is nonstationary (as in mobile radio channels) is studied, and some preliminary ideas for quantizer design are presented     

基于信道编码及空时编码级联的两用户协同分集

传统协同分集通过使网络中各单天线用户共享彼此天线,形成虚拟多天线阵列来实现空间分集,使得体积和功耗受限的网络终端也能获得分集增益,然而这并没有将信道编码和空时编码结合起来以使系统得到编码增益。为了能够获得编码增益来进一步改善系统性能,本文提出了一种基于信道编码和分布式空时分组码级联方式下的两用户协同分集方案,并且在准静态的瑞利衰落信道下对系统误码性能进行了理论推导和系统仿真,给出了误比特率的上限解析表达式。在协同用户间信道存在噪声的情况下,我们分别对CRC-DSTBC和CC-DSTBC级联下的发射方案进行了性能分析和系统仿真。仿真结果表明:即使协同用户间的信道存在噪声,本文所提出的协同分集方案与传统协同分集相比,不但获得了分集增益,同时也得到了编码增益,系统误比特率大大降低,从而显著提高了系统性能,并且这也和理论分析相吻合。     

Graph-Theoretic Complexity Reduction for Markovian Wireless Channel Models

Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this paper, we present a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget. We also demonstrate the implication of unused states in complexity reduction of higher order Markov model. Our trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models.