Complexity analysis of FDE receivers for massive MIMO block transmission systems

Pereira, A. J. S. C.; Bento, Pedro; Gomes, Marco; Dinis, Rui; Silva, Vítor

doi:10.1049/iet-com.2018.5156

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…When changing from a SISO system to a MIMO system, the associated complexity will also be increased, as the number of transmitting and receiving antennas will increase. For the specific case of IB-DFE, this complexity is associated with the need to invert matrices, in order to calculate the F factor, whose dimensions will always be at least R × P. The complexity of this receiver is therefore greater than for the linear case, since one more IDFT/DFT pair exits per iteration [25,26].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…By using Equations ( 14) and ( 25) it is then possible to evaluate the BER performance of a SISO system. When considering a MIMO system, Equation ( 25) should be replaced by Equation (26). Equation ( 26) is fully derived and explained in [23], where the BER performance of receivers that can be used in MIMO systems are analysed and compared.…”

Section: Theoretical Ber Performancementioning

confidence: 99%

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Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements

Fernandes

Cercas

Dinis³

et al. 2020

Applied Sciences

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The demand for ubiquitous telecommunications services forces operators to have a special concern about signal quality and the coverage area they offer to their customers. This was usually checked by using suitable propagation models for Single Input Single Output (SISO) systems, which are no longer the case for new and future mobile generations, such as 5G and beyond. To guarantee good signal quality coverage, operators started to replace these models with Multiple Input Multiple Output (MIMO) ones. To achieve the best results, these models are usually calibrated with Drive Test (DT) measures; however, the DTs available for MIMO propagation models are sparse, in contrast to SISO ones. The main contribution presented in this paper is a methodology to extend the propagation models of SISO systems so they can be applied in MIMO sytems with Single-Carrier and Frequency-Domain Equalization (SC-FDE), while still using DTs acquired for SISO systems. This paper presents the impact on Bit Error Rate (BER) performance and its coverage area resulting from the application of our proposed method. We consider a MIMO SC-FDE system with an Iterative Block Decision Feedback Equalization (IB-DFE) receiver and we present the improvement expressions for the BER that we illustrate with some simulations.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Theoretical Ber Performancementioning

confidence: 99%

Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements

Fernandes

Cercas

Dinis³

et al. 2020

Applied Sciences

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“…The ZF criterion considerably increases the channel noise in sub-channels with local deep notches during the channel equalization process. Using the ZF algorithm, F k becomes [10]:…”

Section: Zero Forcingmentioning

confidence: 99%

“…In addition, the LIS system is reliable and efficient [9]. Four different receivers are studied in this article and compared in terms of performance: namely, Zero Forcing (ZF), Maximum Ratio Combining (MRC), Equal Gain Combining (EGC), and Minimum Mean Squared Error (MMSE) [6,10]. Moreover, low-density parity-check codes (LDPC) coding is studied in the scenario of the LIS system combined with SC-FDE transmission, leading to a reduction in the Bit Error Rate (BER) [11,12].…”

Section: Introduction 1motivationmentioning

confidence: 99%

On the Performance of LDPC-Coded Large Intelligent Antenna System

et al. 2023

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This article studies Large Intelligent Systems (LIS) along with Single Carrier with Frequency Domain Equalization (SC-FDE), utilizing Low-Density Parity-Check (LDPC). Four different receivers are studied in the scenarios described above, namely Equal Gain Combining (EGC), Maximum Ratio Combining (MRC), Zero Forcing (ZF), and Minimum Mean Squared Error (MMSE). The results of this article show that the use of LDPC codes leads to an improvement of performance by about 2 dB for a 4X25 LIS system and by 3 dB for a 4X225 LIS system, as compared to similar systems without LDPC codes. Moreover, for all simulations, the MMSE receiver achieves the best overall performance, while EGC performs the worst.

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“…As a result, keeping receiver complexity low is critical, necessitating low-complexity frequency domain equalization (FDE) detectors, including the MRC and EGC, which do not necessitate inversion of the high-dimensional channel matrix for every frequency part, as needed for the ZF and MMSE receivers, while performing similarly. In order to avoid such matrix inversion, MRC and EGC can be used either as post-processing (at the receiver) or as pre-processing (precoding) components [33,40].…”

Section: System and Signal Characterizationmentioning

confidence: 99%

IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial

2022

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This is a tutorial on current techniques that use a huge number of antennas in intelligent reflecting surfaces (IRS), large intelligent surfaces (LIS), and radio stripes (RS), highlighting the similarities, differences, advantages, and drawbacks. A comparison between IRS, LIS, and RS is performed in terms of the implementation and capabilities, in the form of a tutorial. We begin by introducing the IRS, LIS, and RS as promising technologies for 6 G wireless technology. Then, we will look at how the three notions are applied in wireless networks. We discuss various performance indicators and methodologies for characterizing and improving the performance of IRS, LIS, and RS-assisted wireless networks. We cover rate maximization, power consumption reduction, and cost implementation concerns in order to take advantage of the performance increase. Furthermore, we extend the discussion to some cases of emerging use. In the description of the three concepts, IRS-assisted communication was introduced as a passive system, considering the capacity/data rate, with power optimization being an advantage, while channel estimation was a challenge. LIS is an active component that goes beyond massive MIMO; a recent study found that channel estimation issues in IRS had improved. In comparison to IRS, capacity enhancement is a highlight, and user interference showed a trend of decreasing. However, power consumption due to utilizing power amplifiers has restrictions. The third technique for increasing coverage is cell-free massive MIMO with RS, with easy deployment in communication network structures. It is demonstrated to have suitable energy efficiency and power consumption. Finally, for future work, we further propose expanding the conversation to include some cases of new uses, such as complexity reduction; design and simulation with LDPC code could be a solution to decreasing complexity.

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Complexity analysis of FDE receivers for massive MIMO block transmission systems

Cited by 5 publications

References 30 publications

Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements

Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements

On the Performance of LDPC-Coded Large Intelligent Antenna System

IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial

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