An Investigation of Spectral Efficiency in Linear MRC and MMSE Detectors with Perfect and Imperfect CSI for Massive MIMO Systems

In this paper, the performance of two linear detectors in multi-user (MU) multiple-input multiple-output (MIMO) systems is investigated. The uplink sum rate and lower bound of channel capacity is derived for both maximum-ratio combination (MRC) and minimum mean square error (MMSE) schemes considering imperfect and perfect channel state information (CSI) conditions. Results show that linear detector performance improves dramatically when the number of base station (BS) users is smaller than that of BS antennas. It is being demonstrated that in the case of imperfect CSI and the number of BS antennas in the conditions of perfect CSI the transmitting power of users can be decreased by the square root of the number of BS antennas. Simulation results show that the MMSE detector outperforms the MRC detector. The results indicated that the system's uplink sum rate is increased by using significantly larger antenna arrays as opposed to just one antenna system. The findings of the Monte-Carlo simulation are very close to the analytical results.

NISC-based MIMO MMSE Detector

Several application-specific processor design approaches have been proposed and investigated to cope with the emerging flexibility requirements jointly associated with the maximum performance efficiency and minimum implementation area and power consumption. Dynamic scheduling of a set of instructions generally leads to an overhead related to instruction decoding. To mitigate this overhead, other approaches have been proposed using static scheduling of datapath control signals. In this context, No-Instruction-Set-Computer (NISC) concept have been introduced considering that a dedicated processor to a specific application does not need an instruction set especially when it is programmed by its designers and not by its users. In this paper, the hardware architecture design of flexible NISC-based architecture design dedicated for minimum mean-squared error (MMSE) linear detection is presented. The devised design, which is used in iterative turbo-receiver, fulfills the performance requirements of emergent wireless communication standards with throughput reaching that of LTE-Advanced. FPGA hardware implementation of the detector architecture achieves a maximum throughput of 115.8 Mega symbols per second for [Formula: see text] and 6.4 Mega symbols per second for [Formula: see text] MIMO systems for an operating clock frequency of 202.67[Formula: see text]MHz.

An Improved Jacobi-Based Detector for Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.

Fountain Codes and Linear Filtring to Mitigate Pilot Contamination Issue in Massive MiMo

The fifth generation of cellular mobile (5G) is a future technology to meet growing capacity of users. For this prupose 5G will use advanced technologies. Very large multi- input multi- output or massive MiMo (m-MiMo) is considered as one of the promising technology. Nevertheless, the performance of m-MiMo is limited by pilot contamination issue. In fact, to mitigate pilot contamination issues in massive multi-input multi-output (m-MiMo), we proposed in previous work a new scheme where Raptor decoded symbols are used to estimate channel with Minimum Mean Square Error (MMSE) technique. The main benefit of this method is that the receiver does not need a transmitted pilot symbols to evaluate the channel, which allows saving power at transmission. The results showed that the MMSE scheme achieved the ideal case of the perfect channel. In this precedent paper, the MMSE detector and raptor code are used for their robustness among other schemes of linear detectors, and corrector codes, nevertheless, in case of m-MiMO, it was shown that all linear detectors work optimally. For this purpose, we include in this present article an additional linear filter to enhance the prior study, in which two supplementary detectors are considered, namely Zero Forcing (ZF) and Maximum Ratio Compression (MRC). The objective of this paper is to determine the ideal filtering technique and the robustness fountain code to address pilot contamination problem. In fact, the simulation results show that the ZF can attain the same ideal performance as the MMSE with raptor decoded symbols while MRC achieved lower performance compared to the other two shemes.