Soft-in soft-output detection in the presence of parametric uncertainty via the Bayesian EM algorithm

We investigate the application of the Bayesian expectation-maximization (BEM) technique to the design of soft-in soft-out (SISO) detection algorithms for wireless communication systems operating over channels affected by parametric uncertainty. First, the BEM algorithm is described in detail and its relationship with the well-known expectation-maximization (EM) technique is explained. Then, some of its applications are illustrated. In particular, the problems of SISO detection of spread spectrum, single-carrier and multicarrier space-time block coded signals are analyzed. Numerical results show that BEM-based detectors perform closely to the maximum likelihood (ML) receivers endowed with perfect channel state information as long as channel variations are not too fast

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed

Recursive receivers for diversity channels with correlated flat fading

Copyright © 2003 IEEEThis paper addresses the design and performance of time-recursive receivers for diversity based communication systems with flat Rayleigh or Ricean fading. The paper introduces a general state-space model for such systems, where there is temporal correlation in the channel gain. Such an approach encompasses a wide range of diversity systems such as spatial diversity, frequency diversity, and code diversity systems which are used in practice. The paper describes a number of noncoherent receiver structures derived from both sequence and a posteriori probability-based cost functions and compares their performance using an orthogonal frequency-division multiplex example. In this example, the paper shows how a standard physical delay-Doppler scattering channel model can be approximated by the proposed state-space model. The simulations show that significant performance gains can be made by exploiting temporal, as well as diversity channel correlations. The paper argues that such time-recursive receivers offer some advantages over block processing schemes such as computational and memory requirement reductions and the easier incorporation of adaptivity in the receiver structures.Nguyen, V.K.; White, L.B.; Jaffrot, E.; Soamiadana, M.; Fijalkow, I

Joint CFO Estimation and Data Detection in OFDM systems

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique that is widely used in wireless broadband communication systems. The spectral e ciency of OFDM is very high since the subcarriers are spaced as closely as possible while maintaining orthogonality. However, one of the major problems with OFDM that can cause performance degradation is carrier frequency o set (CFO) which impairs the orthogonality among OFDM subcarriers, as a consequence, results in inter-subcarrier interference. In this thesis, an iterative algorithm for joint CFO estimation and data detection in OFDM systems over frequency selective channels is proposed. The proposed algorithm is performing both CFO estimation and data detection in the frequency domain based on the Expectation-Maximization (EM) algorithm. The proposed algorithm can achieve the same bit-error-rate (BER) performance as that of its time-domain counterpart with much lower complexity. Simulation results show that the proposed algorithm can converge after three iterations and an estimate of CFO can be obtained with high accuracy

EM-Based iterative channel estimation and sequence detection for space-time coded modulation

Reliable detection of signals transmitted over a wireless communication channel requires knowledge of the channel estimate. In this work, the application of expectationmaximization (EM) algorithm to estimation of unknown channel and detection of space-time coded modulation (STCM) signals is investigated. An STCM communication system is presented which includes symbol interleaving at the transmitter and iterative EM-based soft-output decoding at the receiver. The channel and signal model are introduced with a quasi-static and time-varying Rayleigh fading channels considered to evaluate the performance of the communication system. Performance of the system employing Kalman filter with per-survivor processing to do the channel estimation and Viterbi algorithm for sequence detection is used as a reference. The first approach to apply the EM algorithm to channel estimation presents a design of an online receiver with sliding data window. Next, a block-processing EM-based iterative receiver is presented which utilizes soft values of a posteriori probabilities (APP) with maximum a posteriori probability (MAP) as the criterion of optimality in both: detection and channel estimation stages (APP-EM receiver). In addition, a symbol interleaver is introduced at the transmitter which has a great desirable impact on system performance. First, it eliminates error propagation between the detection and channel estimation stages in the receiver EM loop. Second, the interleaver increases the diversity advantage to combat deep fades of a fast fading channel. In the first basic version of the APP-EM iterative receiver, it is assumed that noise variance at the receiver input is known. Then a modified version of the receiver is presented where such assumption is not made. In addition to sequence detection and channel estimation, the EM iteration loop includes the estimation of unknown additive white Gaussian noise variance. Finally, different properties of the APP-EM iterative receiver are investigated including the effects of training sequence length on system performance, interleaver and channel correlation length effects and the performance of the system at different Rayleigh channel fading rates

Communications over fading channels with partial channel information : performance and design criteria

The effects of system parameters upon the performance are quantified under the assumption that some statistical information of the wireless fading channels is available. These results are useful in determining the optimal design of system parameters. Suboptimal receivers are designed for systems that are constrained in terms of implementation complexity. The achievable rates are investigated for a wireless communication system when neither the transmitter nor the receiver has prior knowledge of the channel state information (CSI). Quantitative results are provided for independent and identically distributed (i.i.d.) Gaussian signals. A simple, low-duty-cycle signaling scheme is proposed to improve the information rates for low signal-to-noise ratio (SNR), and the optimal duty cycle is expressed as a function of the fading rate and SNR. It is demonstrated that the resource allocations and duty cycles developed for Gaussian signals can also be applied to systems using other signaling formats. The average SNR and outage probabilities are examined for amplify-and-forward cooperative relaying schemes in Rayleigh fading channels. Simple power allocation strategies are determined by using knowledge of the mean strengths of the channels. Suboptimal algorithms are proposed for cases that optimal receivers are difficult to implement. For systems with multiple transmit antennas, an iterative method is used to avoid the inversion of a data-dependent matrix in decision-directed channel estimation. When CSI is not available, two noncoherent detection algorithms are formulated based on the generalized likelihood ratio test (GLRT). Numerical results are presented to demonstrate the use of GLRT-based detectors in systems with cooperative diversity