Efficient Bayesian Inference for Learning in the Ising Linear Perceptron and Signal Detection in CDMA

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in Code Division Multiple Access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first order transition line that ends in a continuous phase transition point. Finite size effects are also studied.Comment: 11 pages, 3 figure

Gaussian Belief Propagation Based Multiuser Detection

In this work, we present a novel construction for solving the linear multiuser detection problem using the Gaussian Belief Propagation algorithm. Our algorithm yields an efficient, iterative and distributed implementation of the MMSE detector. We compare our algorithm's performance to a recent result and show an improved memory consumption, reduced computation steps and a reduction in the number of sent messages. We prove that recent work by Montanari et al. is an instance of our general algorithm, providing new convergence results for both algorithms.Comment: 6 pages, 1 figures, appeared in the 2008 IEEE International Symposium on Information Theory, Toronto, July 200

Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions

A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.Comment: 11 page

Novel algorithms in wireless CDMA systems for estimation and kernel based equalization

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A powerful technique is presented for joint blind channel estimation and carrier offset method for code- division multiple access (CDMA) communication systems. The new technique combines singular value decomposition (SVD) analysis with carrier offset parameter. Current blind methods sustain a high computational complexity as they require the computation of a large SVD twice, and they are sensitive to accurate knowledge of the noise subspace rank. The proposed method overcomes both problems by computing the SVD only once. Extensive simulations using MatLab demonstrate the robustness of the proposed scheme and its performance is comparable to other existing SVD techniques with significant lower computational as much as 70% cost because it does not require knowledge of the rank of the noise sub-space. Also a kernel based equalization for CDMA communication systems is proposed, designed and simulated using MatLab. The proposed method in CDMA systems overcomes all other methods

Transient behavior of an adaptive synchronous CDMA receiver

A steepest descent algorithm is used to update the adaptive weights of a two-stage synchronous Code-Division Multiple-Access (CDMA) receiver that was proposed recently. An issue of the adaptive CDMA system - the convergence and stability property of the receiver is investigated in this thesis. This adaptive synchronous CDMA receiver uses a decorrelator at the first stage and adopts a neural network which acts as an interference canceler at the second stage. It can achieve near-optimum performance. Furthermore, its computational complexity is just a square function of the number of users. The only requirement is the knowledge of the users\u27 signature sequences. The analysis shows that the algorithm for the adaptive weights is convergent and straightforward in implementation. The guaranteed fast convergence of the receiver weights and the tractable theoretical analysis on it, as revealed in this thesis, make this adaptive receiver a promising approach for wireless communications

Precoded Large Scale Multi-User-MIMO System Using Likelihood Ascent Search for Signal Detection

Multiple antennas at each user equipment (UE) and/or thousands of antennas at the base station (BS) comprise the extremely spectrum efficient large scale multi-user multiple input multiple output system (BS). Due to space constraints, the closely spaced numerous antennas at each UE may cause inter antenna interference (IAI). Furthermore, when one UE comes into contact with another UE in the same cellular network, multi-user interference (MUI) may be introduced to the received signal. To mitigate IAI, efficient precoding pre-coding is necessary at each UE, and the MUI present at the BS can be canceled by efficient Multi-user Detection (MUD) techniques. The majority of earlier literature deal with one or more of these interferences. This paper implements a joint pre-coding and MUD, Lenstra-Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) (LLL-LR-LAS), to mitigate IAI and MUI simultaneously LLL-based LR pre-coding mitigates IAI at each UE, and the LAS algorithm is a neighborhood search-based MUD that cancels BS MUI. The proposed approaches' performance was evaluated using Bit Error Rate analysis, and their complexity were determined using multiplication and addition.Dr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No. 801538. Also, this work was supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional-FEDER-, European Union) under the research Grant PID2021-127409OB-C31 CONDOR. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022)

Multiple-access interference rejecting receivers in DS-CDMA communication system

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN037068 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks

This thesis reports on the application of artificial neural networks to two important problems encountered in cellular communications, namely, location estimation and co-channel interference suppression. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this thesis which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. Location estimation provides users of cellular telephones with information about their location. Some of the existing location estimation techniques such as those used in GPS satellite navigation systems require non-standard features, either from the cellular phone or the cellular network. However, it is possible to use the existing GSM technology for location estimation by taking advantage of the signals transmitted between the phone and the network. This thesis proposes the application of neural networks to predict the location coordinates from signal strength data. New multi-layered perceptron and radial basis function based neural networks are employed for the prediction of mobile locations using signal strength measurements in a simulated COST-231 metropolitan environment. In addition, initial preliminary results using limited available real signal-strength measurements in a metropolitan environment are also reported comparing the performance of the neural predictors with a conventional linear technique. The results indicate that the neural predictors can be trained to provide a near perfect mapping using signal strength measurements from two or more base stations. The second application of neural networks addressed in this thesis, is concerned with adaptive equalization, which is known to be an important technique for combating distortion and Inter-Symbol Interference (ISI) in digital communication channels. However, many communication systems are also impaired by what is known as co-channel interference (CCI). Many digital communications systems such as digital cellular radio (DCR) and dual polarized micro-wave radio, for example, employ frequency re-usage and often exhibit performance limitation due to co-channel interference. The degradation in performance due to CCI is more severe than due to ISI. Therefore, simple and effective interference suppression techniques are required to mitigate the interference for a high-quality signal reception. The current work briefly reviews the application of neural network based non-linear adaptive equalizers to the problem of combating co-channel interference, without a priori knowledge of the channel or co-channel orders. A realistic co-channel system is used as a case study to demonstrate the superior equalization capability of the functional-link neural network based Decision Feedback Equalizer (DFE) compared to other conventional linear and neural network based non-linear adaptive equalizers.This project was funded by Solectron (Scotland) Ltd

Comparison and Performance Analysis of DS-CDMA Systems by Genetic, Neural and GaNN (hybrid) Models

Direct Sequence-Code Division Multiple Access (DS-CDMA) technique is used in cellular systems where users in the cell are separated from each other with their unique spreading codes. DS-CDMA has been used extensively which suffers from multiple access interference (MAI) and inter symbol interference (ISI) due to multipath nature of channels in presence of additive white Gaussian noise (AWGN). Spreading codes play an important role in multiple access capacity of DS-CDMA system and Walsh sequences are used as spreading codes in DS-CDMA. DS CDMA receiver namely genetic algorithm neural network and GaNN (hybrid) based MUD receiver for DS-CDMA communication using Walsh sequences is designed. The performance of the same will be compared among themselves

HpGAN: Sequence Search with Generative Adversarial Networks

Sequences play an important role in many engineering applications and systems. Searching sequences with desired properties has long been an interesting but also challenging research topic. This article proposes a novel method, called HpGAN, to search desired sequences algorithmically using generative adversarial networks (GAN). HpGAN is based on the idea of zero-sum game to train a generative model, which can generate sequences with characteristics similar to the training sequences. In HpGAN, we design the Hopfield network as an encoder to avoid the limitations of GAN in generating discrete data. Compared with traditional sequence construction by algebraic tools, HpGAN is particularly suitable for intractable problems with complex objectives which prevent mathematical analysis. We demonstrate the search capabilities of HpGAN in two applications: 1) HpGAN successfully found many different mutually orthogonal complementary code sets (MOCCS) and optimal odd-length Z-complementary pairs (OB-ZCPs) which are not part of the training set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in wireless communications. 2) HpGAN found new sequences which achieve four-times increase of signal-to-interference ratio--benchmarked against the well-known Legendre sequence--of a mismatched filter (MMF) estimator in pulse compression radar systems. These sequences outperform those found by AlphaSeq.Comment: 12 pages, 16 figure