Bit error rate analysis of 4G communication systems

Padmaja, C.; Malleswari, B. L.

doi:10.1109/wocn.2016.7759874

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…This occurs when bits are altered due to interference, noise, distortion, or synchronization errors. It is also expressed as the ratio of the number of bits in error to the total number of bits transmitted over a given time interval [51].…”

Section: A Performance Metrics Of Massive Mimo Linear Precoding Techn...mentioning

confidence: 99%

“…a) Advantages and Disadvantages of Linear Precoding Techniques Table 1 summarizes the benefits and drawbacks of linear precoding methods [9], [11], [38], [43]- [45], [48], [49], [51].…”

Section: V) Phased Zf Precoding Techniquementioning

confidence: 99%

“…The comparison in Table 2 considers the parameters bit error rate, signal-to-noise ratio, spectral efficiency, and energy efficiency of the linear precoder approaches [9], [11], [38], [43]- [45], [48], [49], [51].…”

Section: C) Comparison Of Massive Mimo Linear Precoding Techniquesmentioning

confidence: 99%

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Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

et al. 2022

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Massive MIMO and mmWave communication are the technologies for achieving 5G design goals. Fortunately, these two technologies share a symbiotic integration. As a result, amalgamating mmWave communications with massive MIMO forms, "mmWave-massive MIMO," which significantly improves spectral and energy efficiency. It also achieves high multiplexing gains and increases mobile network capacity. However, massive MIMO, mmWave communications, and mmWave-massive MIMO systems have been studied independently. Consequently, this article explores the ideas, performances, comparisons, and discussions of these three 5G technologies jointly, considering their precoding and beamforming methods. On the other hand, the complexity of these technologies increases when a large number of antennas and radio frequencies (RFs) are used. Thus, several investigations are going on to search for the appropriate precoding and beamforming strategies with low cost, power, and complexity. Therefore, massive MIMO linear precoding techniques such as zero-forcing, maximum ratio transmission, regularized zero-forcing, truncated polynomial expansion and phased zero forcing are addressed in this work. In addition, the most common non-linear precoding schemes: dirty-paper coding, Tomlinson-Harashima, and vector perturbation, are presented. Furthermore, a detailed discussion of the beamforming techniques called analog, digital, and hybrid analog-digital beamforming schemes is included. We also examine the potential of hybrid analog-digital beamforming with its fully-connected and sub-connected architecture approaches in making mmWave massive MIMO a reality. We evaluate their performance with the parameters: bit error rate, signal to noise ratio, complexity, spectral efficiency, and energy efficiency. According to the analytical and simulation results, the partially-connected hybrid analog/digital beamforming architecture offers better allover performance for mmWave-Massive MIMO communications by compromising: power consumption, cost, complexity, and performance. Finally, the potential future directions in mmWave-massive MIMO precoding and beamforming challenges are addressed.

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Section: A Performance Metrics Of Massive Mimo Linear Precoding Techn...mentioning

confidence: 99%

“…a) Advantages and Disadvantages of Linear Precoding Techniques Table 1 summarizes the benefits and drawbacks of linear precoding methods [9], [11], [38], [43]- [45], [48], [49], [51].…”

Section: V) Phased Zf Precoding Techniquementioning

confidence: 99%

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Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

et al. 2022

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“…In this paper, the type of modulation that is considered is 16-Quadrature Amplitude Modulation (16-QAM). The calculation of BER can be written using (7) depicted below [24].…”

Section: Bit Error Rate (Ber)mentioning

confidence: 99%

Interference Management with Dynamic Resource Allocation Method on Ultra-Dense Networks in Femto-Macrocellular Network

Susanto

Hasim

Fitriawan

2021

JRE

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Ultra-Dense Network (UDN) which is formed from femtocells densely deployed is known as one of key technologies for 5th generation (5G) cellular networks. UDN promises for increased capacity and quality of cellular networks. However, UDN faces more complex interference problems than rarely deployed femtocells, worse on femtocells that are located on cell edge area of macrocell. Therefore, mitigating or reducing effects of interferences is an important issue in UDN. This paper focuses on interference management using dynamic resource allocation for UDN. Types of interference considered in this study are cross-tier (macrocell-to-femtocell) and co-tier (femtocellto-femtocell) interferences for uplink transmission. We consider several scenarios to examine the dynamic resource allocation method for UDN in case of femtocells deployed in the whole area of microcell and in the cell edge area of macrocell. Simulation experiment using MATLAB program has been carried out. The performance parameters that are collected from the simulation are Signal to Interference and Noise Ratio (SINR), throughput, and Bit Error Rate (BER). The obtained simulation results show that system using dynamic resource allocation method outperforms conventional system and the results were consistent for the collected performance parameters. The dynamic resource allocation promises to reduce the effects of interference in UDN.

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“…(Sokolov & Abdullin, ) A channel estimation method based on Kalman filter with STBC codes for multiple‐input multiple‐output (MIMO) systems has been proposed to remove noise on 4G communication channel. (Padmaja & Malleswari, ) A method based on independent component analysis (ICA) has been presented to deal with contaminated ECG signal. (Barhatte et al, ) A new method for image reconstruction using the time‐reversed finite‐difference time‐domain method (TR‐FDTD) in severely noisy environments has been presented.…”

Section: Introductionmentioning

confidence: 99%

An UAV Wireless Communication Noise Suppression Method Based on OFDM Modulation and Demodulation

Jia

Yan

2020

Radio Science

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In view of the problem that the unmanned aerial vehicle (UAV) communication link is vulnerable to strong electromagnetic pulse generated by lightning or high‐voltage transmission lines in the complex electromagnetic environment, the influence of the electromagnetic pulse generated by the high‐voltage transmission line of the EFT electromagnetic pulse and the surge electromagnetic pulse on wireless communication link are analyzed in this paper. When those electromagnetic pulse's noise field strength is 500 V/m, the bit‐error‐rate of the communication link is increased to 45.39%, 45.39%, and 46.10%, respectively, indicating that surge pulse has the strongest influence on the communication link. Therefore, a UAV noise suppression method based on the principle of orthogonal frequency‐division multiplexing (OFDM) modulation and demodulation and dual antenna design has been proposed in this paper. This method is used to filter out electromagnetic impulse noise after wireless signal reception, and then demodulate to achieve the goal of noise suppression. Applying the method to a certain type of UAV based on OFDM modulation and demodulation, and theoretical analysis and simulation results show that the proposed method can effectively suppress the electromagnetic pulse generated by high‐voltage transmission lines, EFT electromagnetic pulse, surge electromagnetic pulse, the bit‐error‐rate dropped to 0.005597%, 0.004531%, 0.005597%, respectively.

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Bit error rate analysis of 4G communication systems

Cited by 8 publications

References 16 publications

Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

Interference Management with Dynamic Resource Allocation Method on Ultra-Dense Networks in Femto-Macrocellular Network

An UAV Wireless Communication Noise Suppression Method Based on OFDM Modulation and Demodulation

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