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EN
In this paper, the performance of a deep learningbased multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Furthermore, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results.
EN
In this work, the performance of selective-decode and forward (S-DF) relay systems over κ-µ fading channel conditions is examined in terms of probability density function (PDF), system model and cumulative distribution function (CDF) of the κ-µ distributed envelope, signal-to-noise ratio and the techniques used to generate samples that rely on κ-µ distribution. Specifically, we consider a case where the sourceto-relay, relay-to-destination and source-to-destination link is subject to independent and identically distributed κ-µ fading. From the simulation results, the enhancement in the symbol error rate (SER) with a stronger line of sight (LOS) component is observed. This shows that S-DF relaying systems may perform well even in non-fading or LOS conditions. Monte Carlo simulations are conducted for various fading parameter values and the outcomes turn out to be a close match for theoretical results, which validates the derivations made.
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