Low-complexity dominance-based Sphere Decoder for MIMO Systems

The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit antennas. We propose an enhanced version of the SD based on an additional cost function derived from conditions on worst case interference, that we call dominance conditions. The proposed detector, the king sphere decoder (KSD), has a computational complexity that results to be not larger than the complexity of the sphere decoder and numerical simulations show that the complexity reduction is usually quite significant

Menekan Tingkat Ber Pada Sistem Komunikasi Direct-Sequence CDMA(DS-CDMA) Menggunakan Jaringan Syaraf Tiruan Transient Chaos

Sistem penerima konvensional pada sistem komunikasi DS-CDMA, terjadi degradasi kinerja akibat perbedaan daya dengan sinyal penginterferensi yang tinggi (Nearto- Far) dan nilai korelasi silang kode user yang berbeda tidak nol, yang mengakibatkan Multiple Access Interference (MAI). Jaringan Saraf Tiruan Transient Chaos (TCNN), sangat potensial untuk mengatasi permasalahan MAI dan Near-to-Far pada penerima konvensional DS-CDMA. Sistem penerima Jaringan Saraf Tiruan Transient Chaos (TCNN) dapat diturunkan dengan memanfaatkan fungsi Likelihood. Dengan fungsi Likelihood tersebut akan diperoleh fungsi energi atau fungsi cost dari sistem penerima multiuser DS-CDMA. Fungsi energi sistem penerima multiuser DS-CDMA diselesaikan dengan algoritma TCNN. Pengujian dilakukan dengan simulasi komputer untuk membandingkan kinerja penerima TCNN dengan konvensional. Hasil simulasi, dapat dilihat bahwa sistem penerima Jaringan Saraf Tiruan Transient Chaos dapat memberikan perbaikan kinerja dibandingkan sistem penerima konvensional (Mathced Filter). Perbaikan kinerja penerima TCNN sebesar 85.092 % pada kondisi E N dB o 6 1 / = , Near-to-Far E E 6dB 2 1 / = , tetapi memerlukan tambahan waktu 0.4845 sekon-per-iterasi algoritma TCNN

A Family of Likelihood Ascent Search Multiuser Detectors: an Upper Bound of Bit Error Rate and a Lower Bound of Asymptotic Multiuser Efficiency

In this paper, the bit error performance of a family of likelihood ascent search (LAS) multiuser detectors is analyzed. An upper bound on the BER of any LAS detector is obtained by bounding the fixed point region with the worst initial detector. The concept of indecomposable errors developed by Verdu is applied to tighten the upper bound. In a special instance, the upper bound is reduced to that for all the local maximum likelihood detectors. The upper bound is comparable with that of the optimum detector obtained by Verdu. A lower bound on the asymptotic multiuser efficiency (AME) is then obtained. It is shown that there are nontrivial CDMA channels such that a LAS detector can achieve unit AME regardless of user number. The AME lower bound provides a means for further seeking a good set of spreading sequences and power distribution for spectral and power efficient CDMA.Comment: To appear in IEEE Trans. on Communication

A Tight Bound for Probability of Error for Quantum Counting Based Multiuser Detection

Future wired and wireless communication systems will employ pure or combined Code Division Multiple Access (CDMA) technique, such as in the European 3G mobile UMTS or Power Line Telecommunication system, but also several 4G proposal includes e.g. multi carrier (MC) CDMA. Former examinations carried out the drawbacks of single user detectors (SUD), which are widely employed in narrowband IS-95 CDMA systems, and forced to develop suitable multiuser detection schemes to increase the efficiency against interference. However, at this moment there are only suboptimal solutions available because of the rather high complexity of optimal detectors. One of the possible receiver technologies can be the quantum assisted computing devices which allows high level parallelism in computation. The first commercial devices are estimated for the next years, which meets the advert of 3G and 4G systems. In this paper we analyze the error probability and give tight bounds in a static and dynamically changing environment for a novel quantum computation based Quantum Multiuser detection (QMUD) algorithm, employing quantum counting algorithm, which provides optimal solution.Comment: presented at IEEE ISIT 2002, 7 pages, 2 figure

Multiuser detection employing recurrent neural networks for DS-CDMA systems.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2006.Over the last decade, access to personal wireless communication networks has evolved to a point of necessity. Attached to the phenomenal growth of the telecommunications industry in recent times is an escalating demand for higher data rates and efficient spectrum utilization. This demand is fuelling the advancement of third generation (3G), as well as future, wireless networks. Current 3G technologies are adding a dimension of mobility to services that have become an integral part of modem everyday life. Wideband code division multiple access (WCDMA) is the standardized multiple access scheme for 3G Universal Mobile Telecommunication System (UMTS). As an air interface solution, CDMA has received considerable interest over the past two decades and a great deal of current research is concerned with improving the application of CDMA in 3G systems. A factoring component of CDMA is multiuser detection (MUD), which is aimed at enhancing system capacity and performance, by optimally demodulating multiple interfering signals that overlap in time and frequency. This is a major research problem in multipoint-to-point communications. Due to the complexity associated with optimal maximum likelihood detection, many different sub-optimal solutions have been proposed. This focus of this dissertation is the application of neural networks for MUD, in a direct sequence CDMA (DS-CDMA) system. Specifically, it explores how the Hopfield recurrent neural network (RNN) can be employed to give yet another suboptimal solution to the optimization problem of MUD. There is great scope for neural networks in fields encompassing communications. This is primarily attributed to their non-linearity, adaptivity and key function as data classifiers. In the context of optimum multiuser detection, neural networks have been successfully employed to solve similar combinatorial optimization problems. The concepts of CDMA and MUD are discussed. The use of a vector-valued transmission model for DS-CDMA is illustrated, and common linear sub-optimal MUD schemes, as well as the maximum likelihood criterion, are reviewed. The performance of these sub-optimal MUD schemes is demonstrated. The Hopfield neural network (HNN) for combinatorial optimization is discussed. Basic concepts and techniques related to the field of statistical mechanics are introduced and it is shown how they may be employed to analyze neural classification. Stochastic techniques are considered in the context of improving the performance of the HNN. A neural-based receiver, which employs a stochastic HNN and a simulated annealing technique, is proposed. Its performance is analyzed in a communication channel that is affected by additive white Gaussian noise (AWGN) by way of simulation. The performance of the proposed scheme is compared to that of the single-user matched filter, linear decorrelating and minimum mean-square error detectors, as well as the classical HNN and the stochastic Hopfield network (SHN) detectors. Concluding, the feasibility of neural networks (in this case the HNN) for MUD in a DS-CDMA system is explored by quantifying the relative performance of the proposed model using simulation results and in view of implementation issues

Menekan Tingkat Ber Pada Sistem Komunikasi Direct-Sequence CDMA(DS-CDMA) Menggunakan Jaringan Syaraf Tiruan Transient Chaos

Sistem penerima konvensional pada sistem komunikasi DS-CDMA, terjadi degradasi kinerja akibat perbedaan daya dengan sinyal penginterferensi yang tinggi (Nearto-Far) dan nilai korelasi silang kode user yang berbeda tidak nol, yang mengakibatkan Multiple Access Interference (MAI). Jaringan Saraf Tiruan Transient Chaos (TCNN), sangat potensial untuk mengatasi permasalahan MAI dan Near-to-Far pada penerima konvensional DS-CDMA. Sistem penerima Jaringan Saraf Tiruan Transient Chaos (TCNN) dapat diturunkan dengan memanfaatkan fungsi Likelihood. Dengan fungsi Likelihood tersebut akan diperoleh fungsi energi atau fungsi cost dari sistem penerima multiuser DS-CDMA. Fungsi energi sistem penerima multiuser DS-CDMA diselesaikan dengan algoritma TCNN. Pengujian dilakukan dengan simulasi komputer untuk membandingkan kinerja penerima TCNN dengan konvensional. Hasil simulasi, dapat dilihat bahwa sistem penerima Jaringan Saraf Tiruan Transient Chaos dapat memberikan perbaikan kinerja dibandingkan sistem penerima konvensional (Mathced Filter). Perbaikan kinerja penerima TCNN sebesar 85.092 % pada kondisi  E1/No = 6dB , Near-to-Far  E2/E1 = 6dB , tetapi memerlukan tambahan waktu  0.4845 sekon-per-iterasi algoritma TCNN. Kata Kunci— Likelihood, Matched Filter, Multiple Access Interference, Near-to-Fa

A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects

Equalization and channel decoding are “traditionally” two cascade processes at the receiver side of a digital transmission. They aim to achieve a reliable and efficient transmission. For high data rates, the energy consumption of their corresponding algorithms is expected to become a limiting factor. For mobile devices with limited battery’s size, the energy consumption, mirrored in the lifetime of the battery, becomes even more crucial. Therefore, an energy-efficient implementation of equalization and decoding algorithms is desirable. The prevailing way is by increasing the energy efficiency of the underlying digital circuits. However, we address here promising alternatives offered by mixed (analog/digital) circuits. We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework. In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI). Based on the proposed model, we show that the superiority of joint equalization and decoding (a well-known fact from the discrete-time case) preserves in analog. Additionally, analog circuit design related aspects such as adaptivity, connectivity and accuracy are discussed and linked to theoretical aspects of recurrent neural networks such as Lyapunov stability and simulated annealing

Mean-field message-passing equations in the Hopfield model and its generalizations

International audienceMotivated by recent progress in using restricted Boltzmann machines as preprocess-ing algorithms for deep neural network, we revisit the mean-field equations (belief-propagation and TAP equations) in the best understood such machine, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polariza-tions of neurons. In the "retrieval phase" where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations : the set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations. This makes the latter method much better suited for applications in the learning process of restricted Boltzmann machines. In the case where the patterns memorized in the Hopfield model are not independent, but are correlated through a combinatorial structure, we show that the TAP equations have to be modified. This modification can be seen either as an alteration of the reaction term in TAP equations, or, more interestingly, as the consequence of message passing on a graphical model with several hidden layers, where the number of hidden layers depends on the depth of the correlations in the memorized patterns. This layered structure is actually necessary when one deals with more general restricted Boltzmann machines