Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models

Abdul-Hadi, Alaa M.; Naser, Marwah Abdulrazzaq; Alsabah, Muntadher; Abdulhussain, Sadiq H.; Mahmmod, Basheera M.

doi:10.7717/peerj-cs.1017

Cited by 5 publications

(3 citation statements)

References 64 publications

(88 reference statements)

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“…According to typical wireless networks, the coherence block length that is available for transmission resources consists of time and frequency symbols. These symbols represent the intervals of time and frequency during which the channel responses remain mostly constant [47][48][49][50]. Extension to orthogonal frequency-division multiple (OFDM) could be considered in the future [51,52].…”

Section: System Model Discussionmentioning

confidence: 99%

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Naser

Abdul-Hadi

Alsabah³

et al. 2023

Electronics

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Future generations of wireless communications systems are expected to evolve toward allowing massive ubiquitous connectivity and achieving ultra-reliable and low-latency communications (URLLC) with extremely high data rates. Massive multiple-input multiple-output (m-MIMO) is a crucial transmission technique to fulfill the demands of high data rates in the upcoming wireless systems. However, obtaining a downlink (DL) training sequence (TS) that is feasible for fast channel estimation, i.e., meeting the low-latency communications required by future generations of wireless systems, in m-MIMO with frequency-division-duplex (FDD) when users have different channel correlations is very challenging. Therefore, a low-complexity solution for designing the DL training sequences to maximize the achievable sum rate of FDD systems with limited channel coherence time (CCT) is proposed using a waterfilling power allocation method. This achievable sum rate maximization is achieved using sequences produced from a summation of the user’s covariance matrices and then applying a waterfilling power allocation method to the obtained low-complexity training sequence. The results show that the proposed TS outperforms the existing methods in the medium and high SNR regimes while reducing computational complexity. The obtained results signify the proposed TS’s feasibility for practical consideration compared with the existing DL training sequence designs.

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Section: System Model Discussionmentioning

confidence: 99%

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Naser

Abdul-Hadi

Alsabah³

et al. 2023

Electronics

Self Cite

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“…Loss functions provide more than just a static illustration of how well your model is doing; they also act as the foundation upon which your algorithms fit data. Most machine learning algorithms have some kind of loss function that is used to find the best parameters (weights) for your data or to optimize them [41]. Importantly, the choice of the loss function is directly related to the activation function used in the output layer of your neural network.…”

Section: Loss Functions and Methodology A Loss Functionsmentioning

confidence: 99%

Developing Novel Robust Loss Functions-Based Classification Layers for DLLSTM Neural Networks

Houran,

Essai Ali,

Abdel-Raman

et al. 2023

IEEE Access

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In this paper, we suggest improving the performance of developed activation functionbased Deep Learning Long Short-Term Memory (DLLSTM) structures by employing robust loss functions like Mean Absolute Error (MAE) and Sum Squared Error (SSE) to create new classification layers. The classification layer is the last layer in any DLLSTM neural network structure where the loss function resides. The LSTM is an improved recurrent neural network that fixes the problem of the vanishing gradient that goes away and other issues. Fast convergence and optimum performance depend on the loss function. Three loss functions (default(Crossentropyex), (MAE) and (SSE)) that compute the error between the actual and desired output for two distinct applications were used to examine the effectiveness of the suggested DLLSTM classifier. The results show that one of the suggested classifiers' specific loss functions(SSE)) works better than other loss functions and does a great job. The suggested functions Softsign, Modified-Elliott, Root-sig, Bi-tanh1, Bi-tanh2, Sech and wave are more accurate than the tanh function.INDEX TERMS DNN, DLLSTM, loss function, mean absolute error, sum squared error.

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“…It is expected that data traffic will continue to strain the capacity of communication networks in the future [ 1 ]. An analysis of current statistics from the International Telecommunication Union (ITU) shows that global mobile data traffic will increase to 607 exabytes (EB) per month by 2025 [ 2 ]. The exponential increase in the 5G throughput requirement drives the spectrum used for the front haul from the conventional microwave band to the millimeter wave (mmWave) spectrum.…”

Section: Introductionmentioning

confidence: 99%

On-Chip Circularly Polarized Circular Loop Antennas Utilizing 4H-SiC and GaAs Substrates in the Q/V Band

Asfour,

Khamas,

Ball

et al. 2024

Sensors

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This paper presents a comprehensive assessment of the performance of on-chip circularly polarized (CP) circular loop antennas that have been designed and fabricated to operate in the Q/V frequency band. The proposed antenna design incorporates two concentric loops, with the outer loop as the active element and the inner loop enhancing the CP bandwidth. The study utilizes gallium arsenide (GaAs) and silicon carbide (4H-SiC) semiconductor wafer substrates. The measured results highlight the successful achievement of impedance matching at 40 GHz and 44 GHz for the 4H-SiC and GaAs substrates, respectively. Furthermore, both cases yield an axial ratio (AR) of less than 3 dB, with variations in bandwidths and frequency bands contingent upon the dielectric constant of the respective substrate material. Moreover, the outcomes confirm that utilizing 4H-SiC substrates results in a significantly higher radiation efficiency of 95%, owing to lower substrate losses. In pursuit of these findings, a 4-element circularly polarized loop array antenna has been fabricated for operation at 40 GHz, employing a 4H-SiC wafer as a low-loss substrate. The results underscore the antenna’s remarkable performance, exemplified by a broadside gain of approximately 9.7 dBic and a total efficiency of circa 92%. A close agreement has been achieved between simulated and measured results.

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Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models

Cited by 5 publications

References 64 publications

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Developing Novel Robust Loss Functions-Based Classification Layers for DLLSTM Neural Networks

On-Chip Circularly Polarized Circular Loop Antennas Utilizing 4H-SiC and GaAs Substrates in the Q/V Band

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