Blind equalization of DS-CDMA and MC-CDMA modulations in time-variant frequency selective channels

The paper addresses the blind equalization problem of spread spectrum modulations in the presence of fast time-variant frequency-selective channels. The basic assumption of the paper is that the channel response exhibits fast changes. A second goal of the paper is to force the definition of a universal CDMA blind equalization scheme that is capable of performing for DS-CDMA or multi-carrier CDMA signal modulations without any modification in the equalizer. The formulation of the equalization scheme allows the consideration of temporal and/or spatial diversity front-end receivers. The result is a high-performance system that uses a deterministic blind criterion to equalize the channel, avoiding the use of stochastic methods. The proposed technique performs direct channel equalization without previous channel estimation. Although the proposed equalizer in this work performs equalization at chip rate, this paper suggests a solution to achieve equalization at lower rates.Peer ReviewedPostprint (published version

A genetic algorithm-assisted semi-adaptive MMSE multi-user detection for MC-CDMA mobile communication systems

In this work, a novel Minimum-Mean Squared-Error (MMSE) multi-user detector is proposed for MC-CDMA transmission systems working over mobile radio channels characterized by time-varying multipath fading. The proposed MUD algorithm is based on a Genetic Algorithm (GA)-assisted per-carrier MMSE criterion. The GA block works in two successive steps: a training-aided step aimed at computing the optimal receiver weights using a very short training sequence, and a decision-directed step aimed at dynamically updating the weights vector during a channel coherence period. Numerical results evidenced BER performances almost coincident with ones yielded by ideal MMSE-MUD based on the perfect knowledge of channel impulse response. The proposed GA-assisted MMSE-MUD clearly outperforms state-of-the-art adaptive MMSE receivers based on deterministic gradient algorithms, especially for high number of transmitting users

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Adaptive DSP Algorithms for UMTS: Blind Adaptive MMSE and PIC Multiuser Detection

A study of the application of blind adaptive Minimum Mean Square Error (MMSE) and Parallel Interference Cancellation (PIC) multiuser detection techniques to Wideband Code Division Multiple Access (WCDMA), the physical layer of Universal Mobile Telecommunication System (UMTS), has been performed as part of the Freeband Adaptive Wireless Networking project. This study was started with an analysis of Code Division Multiple Access (CDMA) and conventional CDMA detection. After that blind adaptive MMSE and PIC detection have been analyzed for general CDMA systems. Then the differences between WCDMA and general CDMA were analyzed and the results have been used to determine how blind adaptive MMSE and PIC can be implemented in WCDMA systems. Blind adaptive MMSE has been implemented inWCDMASim, aWCDMA simulator and some preliminary simulation results obtained with this simulator are presented. These simulation results do not yet show the performance that was expected of blind adaptive MMSE detection based on simulation results obtained in previous research. The cause for these unexpected results is not yet known and will be the subject of further research.\ud Implementation of PIC detection in WCDMASim was found to require changes to the architecture of the WCDMASim simulator. Implementation of these changes and solving the problems with blind adaptive MMSE detection are considered for future work

DSP Prototyping of Blind Adaptive MMSE Multiuser Detection for Cellular Wireless CDMA Systems

Blind adaptive Minimum Mean Square Error (MMSE) detection is theoretically one of the most promising multiuser detection techniques for cellular wireless Code-Division Multiple Access (CDMA) systems, but its implementation has not yet been studied extensively. Therefore the goal of the research described in this paper is to study the implementation of blind adaptive MMSE detection on the current generation of DSPs and to determine the detectedbits-per-second performance that can be achieved by such an implementation. The blind adaptive MMSE detection algorithm is first analyzed in order to determine how it can be implemented. The algorithm is then implemented in a simulator and the simulator is used to study the adaptive behavior of the algorithm. The simulator is also used to verify the correctness of the implementation of the algorithm by comparing the simulation results obtained with the simulator to simulation results published in literature. When the algorithm is shown to be correct it is implemented on and optimized for a floating-point DSP. This DSP implementation is used to determine the detected-bits-per-second performance that can be achieved by blind adaptive MMSE detection on modern DSPs

New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications

An ABORT-like detector with improved mismatched signals rejection capabilities

In this paper, we present a GLRT-based adaptive detection algorithm for extended targets with improved rejection capabilities of mismatched signals. We assume that a set of secondary data is available and that noise returns in primary and secondary data share the same statistical characterization. To increase the selectivity of the detector, similarly to the ABORT formulation, we modify the hypothesis testing problem at hand introducing fictitious signals under the null hypothesis. Such unwanted signals are supposed to be orthogonal to the nominal steering vector in the whitened observation space. The performance assessment, carried out by Monte Carlo simulation, shows that the proposed dectector ensures better rejection capabilities of mismatched signals than existing ones, at the price of a certain loss in terms of detection of matched signals

Regularized Gradient Descent: A Nonconvex Recipe for Fast Joint Blind Deconvolution and Demixing

We study the question of extracting a sequence of functions $\{\boldsymbol{f}_i, \boldsymbol{g}_i\}_{i=1}^s$ from observing only the sum of their convolutions, i.e., from $\boldsymbol{y} = \sum_{i=1}^s \boldsymbol{f}_i\ast \boldsymbol{g}_i$. While convex optimization techniques are able to solve this joint blind deconvolution-demixing problem provably and robustly under certain conditions, for medium-size or large-size problems we need computationally faster methods without sacrificing the benefits of mathematical rigor that come with convex methods. In this paper, we present a non-convex algorithm which guarantees exact recovery under conditions that are competitive with convex optimization methods, with the additional advantage of being computationally much more efficient. Our two-step algorithm converges to the global minimum linearly and is also robust in the presence of additive noise. While the derived performance bounds are suboptimal in terms of the information-theoretic limit, numerical simulations show remarkable performance even if the number of measurements is close to the number of degrees of freedom. We discuss an application of the proposed framework in wireless communications in connection with the Internet-of-Things.Comment: Accepted to Information and Inference: a Journal of the IM