Low-complexity MMSE detector for massive MIMO systems based on Damped Jacobi method

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications.

show abstract

“…Minango et al [111] Proposed a Damped Jacobi method for the reduction of complexity MMSE detector algorithm in their study.…”

Section: Minango Et Al [110]mentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…By taking advantage of the M-MIMO properties, some algorithms to reduce the complexity of linear detectors are proposed in [10] and [11]. In particular, in [10] is proposed a low-complexity MMSE detector algorithm based 1 The performance is similar to the Maximum-Likelihood detector on the Damped Jacobi method to determine the optimum and quasi-optimum damped parameter by exploiting the massive MIMO channel property of asymptotic orthogonality.…”

Section: Introductionmentioning

confidence: 99%

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

et al. 2019

Self Cite

View full text Add to dashboard Cite

New perspectives for wireless communications have brought new techniques, such as a very large number of antennas at a base station (BS) serving multiple user terminals (UTs) with a single antenna each, known as massive MIMO (M-MIMO). M-MIMO linear detectors, such as maximal-ratio combining (MRC), zero-forcing (ZF) or minimum-mean-square error (MMSE) can achieve excellent performance with low complexity due to the channel hardening property. However, imperfect channel estimation produces a penalty in the performance. An average bit error rate (BER) performance analysis over time-invariant channel is presented for M-MIMO systems under imperfect channel estimation in contrast with most of M-MIMO literature that uses the ergodic capacity approach. Closed-form expressions and bounds to evaluate the average BER are derived for MRC, ZF and MMSE detectors in a unicellular environment considering M -QAM modulation. Furthermore, an expression to evaluate the normalized signal-to-noise ratio (E b /N 0 ) penalty due to the imperfect channel estimation is presented. Montecarlo numerical simulations are used to verify the tightness of the derived equations which are a function of the number of BS antennas, number of users, coherence time interval, number of pilot symbols and the E b /N 0 of pilot and data symbols used for channel estimation and data detection.INDEX TERMS BER, imperfect channel estimation, massive MIMO, maximal-ratio combining, minimummean-square error, zero-forcing.

show abstract

“…In layman terms, the proposed iterative algorithm expression can be easily deduced from the combination of the equations (13) and (17) as follows:…”

Section: Proposed Methodsmentioning

confidence: 99%

“…A decision-aided Jacobi (DA-Jacobi) iteration has been proposed in [16], this proposition can extensively improve the convergence speed as compared to the traditional Jacobi, and at the same time, may result in lower computational complexity. By providing a damping factor [17], a damped Jacobi method has been appeared lately to reduce the computational complexity of the classical MMSE detector, and to ensure the fast convergence thanks to the multiplication of the initial solution by a reasonable damping factor.…”

Section: Introductionmentioning

confidence: 99%

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Naceur

2021

J. Microw. Optoelectron. Electromagn. Appl.

View full text Add to dashboard Cite

For massive multiple-input multiple-output (m-MIMO) uplink, the performances of the linear minimum mean-square error (MMSE) detector are considered near optimal, and they occupy benchmark place for most linear iterative detectors. However, the MMSE algorithm is known by its load computational complexity due to the implication of large-scale matrix inversions, and in other hand, iterative methods are often preferred in signal detection because of its low complexity. In this paper, we propose a New Damped Jacobi (NDJ) detector in order to improve the performance of the classical Jacobi linear algorithm. Starting from the classical Jacobi technique to our new proposal, we go through the development of two variants; one uses a damping factor and the other uses a stair-matrix. However, the NDJ incorporates a damping factor in its construction and basing also on stair matrix instead of diagonal matrix. The performances in terms of convergence and low complexity of each Jacobi variant studied in this paper are analyzed. Finally, some simulation examples are given to illustrate the advantages of the new proposed algorithm.

show abstract

Low-complexity MMSE detector for massive MIMO systems based on Damped Jacobi method

Cited by 26 publications

References 10 publications

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Contact Info

Product

Resources

About