Likelihood Ascent Search Detector Research Articles

Fractional sequential likelihood ascent search detector for interference cancelation in massive MIMO systems in 5G technology

ABSTRACT Interference is considered as the major problem in the next-generation wireless systems to attain higher throughput. The conventional interference cancelation techniques attain a theoretical breach in endorsing the wireless systems. However, the interference in the signals causes an error or imprecise estimation of the channel for future fifth-generation (5 G) transmissions. Thus, this paper proposes a technique, named Fractional Sequential Likelihood Ascent Search (Fractional-SLAS) for mitigating the interference in massive Multiple Input Multiple Output (MIMO) systems in 5 G technology. The proposed Fractional-SLAS is the integration of Fractional calculus (FC) and Sequential Likelihood Ascent Search Detector (SLAS). Initially, the signals are transmitted through the transmitter and undergo the encoding and modulation process. Then, the obtained modulated signals are fetched using the transmitting antenna that transmits the signals to the receiver. At the receiver side, interference is generated due to the number of cross antennas. Thus, the interference in the signals is eradicated by applying the proposed Fractional-SLAS at the receiver end. Hence, the proposed interference mitigation method is completely eradicated from the signals without any delay or loss in the quality of the transmitted signal. The proposed Fractional-SLAS provides superior performance with minimal Bit Error Rate (BER), Symbol Error Rate (SER), and throughput with values 0.00006 dB, 0.00006 dB, and 0.96 bps.

LAS Detector with Soft-Output MMSE Initialization Under Imperfect Channel Estimation and Channel Correlation

This paper proposes a modified likelihood ascent search (LAS) algorithm for multiple-input multiple-output (MIMO) systems under correlated channel and imperfect channel state information. We assume that the LAS detector is initialized by the minimum mean square error (MMSE) filter. We propose a new maximum-likelihood (ML) decoding metric by considering the soft-output of the MMSE filter. Our proposed LAS takes both channel estimation errors and spatial correlation of antennas into account when computing the ML metric. We show that, while increasing the computational complexity, the proposed LAS remains suitable for massive MIMO systems. Simulation results show that the proposed LAS outperforms the conventional LAS detector, specially when the transmitter correlation increases.

On Optimality of Local Maximum-Likelihood Detectors in Large-Scale MIMO Channels

The replica method originated from statistical mechanics has been successfully applied to analyzing performance of the global maximum-likelihood (GML) MIMO detector in the large-system limit. In this paper, the analysis is extended to the local maximum-likelihood (LML) detectors. A bit error rate (BER) formula for the LML detectors with a fixed neighborhood size is obtained by the replica method and interestingly by the method of Gaussian approximation as well. It is shown that the LML BER is always one of the solutions to the GML BER in any system configuration. Furthermore, the LML BER is the only solution of the GML BER in a broad range of system parameters of practical interest. In the high signal-to-noise ratio regime, both LML and GML detectors achieve the AWGN channel performance when the channel load is up to 1.51 bits/dimension with an equal-energy distribution, and the load can be higher with an unequal-energy distribution. This analytical result is verified by simulation that the sequential likelihood ascent search detector, which is a linear-complexity LML detector, can approach the BER of the NP-hard GML detector predicted by the analysis. This result might be practically useful in large MIMO systems.

A Randomized Space-Time Spreading Scheme for Massive MIMO Channels

We consider a wireless transmission scheme based on randomized space-time spreading (STS) systems over massive multiple-input multiple-output (MIMO) channels. In the systems, the signals are spread by spreading matrices over time and antenna domains at the transmitter. We propose that the spreading matrices are generated from binary pseudo-noise (PN) and mutual quasi-orthogonal sequences, by partitioning each of the sequences into subsequences treated as real and imagination parts of rows or columns of a spreading matrix. We give a likelihood ascent search (LAS) detector for the STS systems and analyze its computational complexity. The number of multiplication and division operations for the LAS detector does not depend on its iterative process and the number of chips in the bit period. We use Monte Carlo method to solve the bit error rates (BERs) of the STS systems and to count numbers of evaluating bits in the LAS detectors. We choose the system parameters, such as the number of chips in the bit period and the transmission bit rates, according to BER performance and/or the numbers of evaluating bits attained from Monte-Carlo method. With the spreading matrices, the detection, and the suitable parameters, the STS systems achieve BERs near to their single-bit performance, which is the BER of a STS system with a single bit spread by orthonormal sequences at each transmitting antenna. We also compare our systems with V-BLAST-like spatial multiplexing systems with BPSK signals to show the improvement of the BER performance.