Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise

Naser, Marwah Abdulrazzaq; Alsabah, Muntadher; Mahmmod, Basheera M.; Noordin, Nor Kamariah; Abdulhussain, Sadiq H.; Baker, Thar

doi:10.3390/electronics9122155

Cited by 14 publications

(16 citation statements)

References 58 publications

(108 reference statements)

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“…As a direction of future work, we consider extending the proposed solutions to uplink OFDMA with MU-MIMO. Such an extension is complicated by many issues, including timely sounding, CSI estimation [ 27 , 35 ] and overhead reduction. Furthermore, we plan to extend our results to a bit more flexible RU structure of 802.11be [ 36 ], which is currently being developed.…”

Section: Discussionmentioning

confidence: 99%

IEEE 802.11ax OFDMA Resource Allocation with Frequency-Selective Fading

Tutelian

Bankov

Shmelkin³

et al. 2021

Sensors

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This paper studies the usage of orthogonal frequency division multiple access (OFDMA) for uplink transmissions in IEEE 802.11ax networks. OFDMA enables simultaneous multi-user transmissions in Wi-Fi, but its usage requires efficient resource allocation algorithms. These algorithms should be able to adapt to the changing channel conditions, including the frequency-selective fading. This paper presents an OFDMA resource allocation algorithm for channels with frequency-selective fading and proposes an approach to adapt the user transmission power and modulation and coding schemes to the varying channel conditions, which is efficient even in the case when the access point has outdated channel state information. The proposed scheduling algorithm and power allocation approach can double the goodput and halve the data transmission time in Wi-Fi networks even in dense deployments of access points.

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Section: Discussionmentioning

confidence: 99%

IEEE 802.11ax OFDMA Resource Allocation with Frequency-Selective Fading

Tutelian

Bankov

Shmelkin³

et al. 2021

Sensors

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“…The hybrid polynomials show a remarkable performance improvement in terms of energy compaction and localization properties in comparison to any sorts of orthogonal polynomials, i.e., Krawtchouk-Tchebichef polynomials only [33]. Such polynomial combinations could also be efficiently applied in communication signal processing to reduce the complexity of RZF and RZFBF precoders [34,35] or to minimize the feedback overhead [36,37]. To this end, this paper proposes an approach based on SKTP aiming to provide a fast handwritten numeral recognition with high accuracy.…”

Section: Orthogonal Polynomialsmentioning

confidence: 99%

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Abdulhussain

Mahmmod

Naser

et al. 2021

Sensors

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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.

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“…In a block-fading model, the channels are considered to be frequency flat, which remain unchanged during a constant period of time that corresponds to a channel coherence time of T [29]. Two common transmission modes are used in the current generation of wireless communications systems, namely time-division duplex (TDD) and frequency-division duplex (FDD) [30][31][32][33]. The canonical m-MIMO systems are typically considered to operate in TDD mode, where the uplink and downlink transmissions share the same frequency band.…”

Section: System Modelmentioning

confidence: 99%

The Role of Correlation in the Performance of Massive MIMO Systems

Naser

Salman

Alsabah

2021

ASI

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Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realistic physical channel model. To this end, the MSE minimization and the spectral efficiency (SE) maximization are investigated. The results show that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated channels even though the MSE is considerably minimized. This implies that using a URA topology with relatively high channel correlations would not be beneficial to the SE unless an interference management scheme is exploited.

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Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise

Cited by 14 publications

References 58 publications

IEEE 802.11ax OFDMA Resource Allocation with Frequency-Selective Fading

IEEE 802.11ax OFDMA Resource Allocation with Frequency-Selective Fading

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

The Role of Correlation in the Performance of Massive MIMO Systems

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