Minimum Bit-Error Rate Design for Space-Time Equalisation-Based Multiuser Detection

A novel minimum bit-error rate (MBER) space–time equalization (STE)-based multiuser detector (MUD) is proposed for multiple-receive-antenna-assisted space-division multiple-access systems. It is shown that the MBER-STE-aided MUD significantly outperforms the standard minimum mean-square error design in terms of the achievable bit-error rate (BER). Adaptive implementations of the MBER STE are considered, and both the block-data-based and sample-by-sample adaptive MBER algorithms are proposed. The latter, referred to as the least BER (LBER) algorithm, is compared with the most popular adaptive algorithm, known as the least mean square (LMS) algorithm. It is shown that in case of binary phase-shift keying, the computational complexity of the LBER-STE is about half of that required by the classic LMS-STE. Simulation results demonstrate that the LBER algorithm performs consistently better than the classic LMS algorithm, both in terms of its convergence speed and steady-state BER performance. Index Terms—Adaptive algorithm, minimum bit-error rate (MBER), multiuser detection (MUD), space–time processing

Determining the optimal decision delay parameter for a linear equalizer

The achievable bit error rate of a linear equalizer is crucially determined by the choice of a decision delay parameter. This brief paper presents a simple method for the efficient determination of the optimal decision delay parameter that results in the best bit error rate performance for a linear equaliz

Adaptive MBER space-time DFE assisted multiuser detection for SDMA systems

In this contribution we propose a space-time decision feedback equalization (ST-DFE) assisted multiuser detection (MUD) scheme for multiple antenna aided space division multiple access systems. A minimum bit error rate (MBER) design is invoked for the MUD, which is shown to be capable of improving the achievable bit error rate performance over that of the minimum mean square error (MMSE) design. An adaptive MBER ST-DFE-MUD is proposed using the least bit error rate algorithm, which is demonstrated to consistently outperform the least mean square (LMS) algorithm, while achieving a lower computational complexity than the LMS algorithm for the binary signalling scheme. Simulation results demonstrate that theMBER ST-DFE-MUD is more robust to channel estimation errors as well as to error propagation imposed by decision feedback errors, compared to the MMSE ST-DFE-MUD

A 6.0-mW 10.0-Gb/s Receiver With Switched-Capacitor Summation DFE

A low-power receiver with a one-tap decision feedback equalization (DFE) was fabricated in 90-nm CMOS technology. The speculative equalization is performed using switched-capacitor-based addition at the front-end sample-hold circuit. In order to further reduce the power consumption, an analog multiplexer is used in the speculation technique implementation. A quarter-rate-clocking scheme facilitates the use of low-power front-end circuitry and CMOS clock buffers. The receiver was tested over channels with different levels of ISI. The signaling rate with BER<10^-12 was significantly increased with the use of DFE for short- to medium-distance PCB traces. At 10-Gb/s data rate, the receiver consumes less than 6.0 mW from a 1.0-V supply. This includes the power consumed in all quarter-rate clock buffers, but not the power of a clock recovery loop. The input clock phase and the DFE taps are adjusted externally

56+ Gb/s serial transmission using duo-binary signaling

In this paper we present duobinary signaling as an alternative for signaling schemes like PAM4 and Ensemble NRZ that are currently being considered as ways to achieve data rates of 56 Gb/s over copper. At the system level, the design includes a custom transceiver ASIC. The transmitter is capable of equalizing 56 Gb/s non-return to zero (NRZ) signals into a duobinary response at the output of the channel. The receiver includes dedicated hardware to decode the duobinary signal. This transceiver is used to demonstrate error-free transmission for different PCB channel lengths including a state-of-the-art Megtron 6 backplane demonstrator

Linear MMSE-Optimal Turbo Equalization Using Context Trees

Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.Comment: Submitted to the IEEE Transactions on Signal Processin

Multiple-antenna-aided OFDM employing genetic-algorithm-assisted minimum bit error rate multiuser detection

The family of minimum bit error rate (MBER) multiuser detectors (MUD) is capable of outperforming the classic minimum mean-squared error (MMSE) MUD in terms of the achievable bit-error rate (BER) owing to directly minimizing the BER cost function. In this paper,wewill invoke genetic algorithms (GAs) for finding the optimum weight vectors of the MBER MUD in the context of multiple-antenna-aided multiuser orthogonal frequency division multiplexing (OFDM) .We will also show that the MBER MUD is capable of supporting more users than the number of receiver antennas available, while outperforming the MMSE MUD