Multiuser Detection and Channel Estimation for Multibeam Satellite Communications

In this paper, iterative multi-user detection techniques for multi-beam communications are presented. The solutions are based on a successive interference cancellation architecture and a channel decoding to treat the co-channel interference. Beams forming and channels coefficients are estimated and updated iteratively. A developed technique of signals combining allows power improvement of the useful received signal; and then reduction of the bit error rates with low signal to noise ratios. The approach is applied to a synchronous multi-beam satellite link under an additive white Gaussian channel. Evaluation of the techniques is done with computer simulations, where a noised and multi-access environment is considered. The simulations results show the good performance of the proposed solutions.Comment: 12 page

On the Creation of Diverse Ensembles for Nonstationary Environments using Bio-inspired Heuristics

Recently the relevance of adaptive models for dynamic data environments has turned into a hot topic due to the vast number of sce- narios generating nonstationary data streams. When a change (concept drift) in data distribution occurs, the ensembles of models trained over these data sources are obsolete and do not adapt suitably to the new distribution of the data. Although most of the research on the field is focused on the detection of this drift to re-train the ensemble, it is widely known the importance of the diversity in the ensemble shortly after the drift in order to reduce the initial drop in accuracy. In a Big Data sce- nario in which data can be huge (and also the number of past models), achieving the most diverse ensemble implies the calculus of all possible combinations of models, which is not an easy task to carry out quickly in the long term. This challenge can be formulated as an optimization prob- lem, for which bio-inspired algorithms can play one of the key roles in these adaptive algorithms. Precisely this is the goal of this manuscript: to validate the relevance of the diversity right after drifts, and to un- veil how to achieve a highly diverse ensemble by using a self-learning optimization technique

Fixed-complexity quantum-assisted multi-user detection for CDMA and SDMA

In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multilevel K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses MK CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-Høyer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems

Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance

Iterative multiuser detection and channel decoding for DS-CDMA using Harmony Search

A novel random-guided optimization method is proposed for multiuser detection (MUD) in DS-CDMA systems employing the so-called Harmony Search (HS) algorithm. We specifically design the HS-aided MUD for the communications problem considered and apply it in an iterative joint MUD and channel decoding framework. Our simulation results demonstrate that a near-single-user performance can be achieved without the employment of the full-search-based optimum MUD even in extremely highly loaded DS-CDMA systems