Optimum Combiner for Spatially Correlated Nakagami-<i>m</i> Fading Channels

Lin, Jia-Chin; Poor, H. Vincent

doi:10.1109/twc.2020.3028284

Cited by 5 publications

(4 citation statements)

References 42 publications

(76 reference statements)

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“…The MC results are plotted as black square markers in Figs. [6][7][8][9][10]. Overall, our estimated values show good agreement with the MC simulations.…”

Section: Numerical Results and Discussionsupporting

confidence: 79%

SER performance analysis of generalized κ — μ and η — μ fading channels

Guo,

Zhang,

You

et al. 2024

J. Commun. Netw.

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System performance analysis is a vital issue in the fifth generation (5G) and beyond wireless communications, which is widely adopted for the design and estimation of wireless communication systems before deployments. To this end, the versatile system performance expressions that can be used under various conditions are of significant importance. This paper investigates the symbol error rate (SER) performance over the generalized κ − µ and η − µ fading channels, whereby the SER expressions in this case are versatile and can characterize the performance under various wireless channel models. To address the computational complexity associated with highorder trigonometric integration, we present the closed-form SER expressions with arbitrary small errors. Monte Carlo-based simulations demonstrate the validity of our derivation and analysis. Simulation results also show that: 1) the closed-form solutions we derived for SER yield minimal errors upon variations in the truncation factor T and are computationally more efficient, in which T can be set to a minimal value to attain precise outcomes or be optimally chosen contingent on the channel parameters; 2) elevating the values of parameters κ, µ and η results in decreased SER, with µ exerting a more significant impact than κ and η; and 3) our approximate expressions have superior accuracy compared to the previous estimated expressions.

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“…The MC results are plotted as black square markers in Figs. [6][7][8][9][10]. Overall, our estimated values show good agreement with the MC simulations.…”

Section: Numerical Results and Discussionsupporting

confidence: 79%

SER performance analysis of generalized κ — μ and η — μ fading channels

Guo,

Zhang,

You

et al. 2024

J. Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…Figure 10 provides the comparison between the proposed ITRS system and other existing systems like ORN, ML-ORSM, and OPAS. Te suggested ITRS scheme, with an SNR of 36 dB, is shown to give the maximum SNR and minimum BER as 10 −6 when compared with the other existing systems [54][55][56][57][58][59]. Te numerical values of such comparisons are given in Table 6.…”

Section: Simulation Resultsmentioning

confidence: 99%

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

Rathika

Poruran

Kumar

et al. 2022

Mathematical Problems in Engineering

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In recent years, cooperative communication (CC) technology has emerged as a hotspot for testing wireless communication networks (WCNs), and it will play an important role in the spectrum utilization of future wireless communication systems. Instead of running node transmissions at full capacity, this design will distribute available paths across multiple relay nodes to increase the overall throughput. The modeling WCNs coordination processes, as a recurrent mechanism and recommending a deep learning-based transfer choice, propose a recurrent neural network (RNN) process-based relay selection in this research article. This network is trained according to the joint receiver and transmitter outage likelihood and shared knowledge, and without the use of a model or prior data, the best relay is picked from a set of relay nodes. In this study, we make use of the RNN to do superdimensional (high-layered) processing and increase the rate of learning and also have a neural network (NN) selection testing to study the communication device, find out whether or not it can be used, find out how much the system is capable of, and look at how much energy the network needs. In these simulations, it has been shown that the RNN scheme is more effective on these targets and allows the design to keep converged over a longer period of time. We will compare the accuracy and efficiency of our RNN processed-based relay selection methods with long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BLSTM),which are all acronyms for long short-term memory methods.

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“…Let the independent channels have identical amplitudes such that θ ¼ 45 0 , and let z k be the transmitted symbol. For a two-branch system with the inputs b s 1 and b s 2 , we can use Equation (10) to express the signals at the output of the decorrelator as b v ¼ Q H b s, which is expressed as follows [27]:…”

Section: Basis Signal Synthesizermentioning

confidence: 99%

“…Receive diversity approaches are recognized to increase numerous figures of merit in wireless communications systems by providing redundant signals that can be combined to achieve a better quality of service (QoS) [1][2][3][4][5][6][7][8]. However, space restrictions, e.g., in mobile handsets make it difficult to receive uncorrelated signals, hence the need for decorrelation [3,[9][10][11]. In fact, several instantaneous decorrelation algorithms are known, but signal information measurements are required.…”

Section: Introductionmentioning

confidence: 99%

Gain of Spatial Diversity with Conjoint Signals in Arbitrarily Correlated Rayleigh Fading Channels

Omosa,

Akuon,

et al. 2023

Wireless Communications and Mobile Computing

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Coding gains for arbitrarily correlated signals in a spatial diversity system with conjoint signals are presented in this study. The basic form of the proposed signal synthesizer evenly produces phase changes in the output signals. The mixer is an orthogonal transformation matrix, which is energy preserving and blind to the channel correlation matrix. The idea is to synthesize additional conjoint signal copies from the received signals that would be received if there were more antennas. However, these conjoint signals contain a level of correlation with the received signals. With the assumption of flat Rayleigh fading channels, simulation results for symbol error probability (SEP) are presented for different numbers of receive branches and varying correlation conditions. It is shown that under binary phase shift keying (BPSK), the synthesizer achieves decorrelation coding gains of about 1 dB when selection combining (SC) or equal gain combining (EGC) is used. The synthesizer’s performance across M-ary quadrature amplitude modulation (MQAM) signals is also tested. In addition, analytical frameworks are derived for BPSK and MQAM, which are tightly bound by the Monte Carlo simulation results obtained using Matlab. The correlation analysis is performed for different numbers of antennas and varied antenna spacings.

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Optimum Combiner for Spatially Correlated Nakagami-m Fading Channels

Cited by 5 publications

References 42 publications

SER performance analysis of generalized κ — μ and η — μ fading channels

SER performance analysis of generalized κ — μ and η — μ fading channels

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

Gain of Spatial Diversity with Conjoint Signals in Arbitrarily Correlated Rayleigh Fading Channels

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